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  • Articles  (1,811)
  • 2015-2019  (1,811)
  • 1945-1949
  • 2016  (1,811)
  • Bioinformatics  (785)
  • IEEE Transactions on Knowledge and Data Engineering  (242)
  • 1274
  • 2184
  • Computer Science  (1,811)
  • 1
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    ll-ACHRB: a scalable algorithm for sampling the feasible solution space of metabolic networks (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: Random sampling of the solution space has emerged as a popular tool to explore and infer properties of large metabolic networks. However, conventional sampling approaches commonly used do not eliminate thermodynamically unfeasible loops. Results: In order to overcome this limitation, we developed an efficient sampling algorithm called loopless Artificially Centered Hit-and-Run on a Box (ll-ACHRB). This algorithm is inspired by the Hit-and-Run on a Box algorithm for uniform sampling from general regions, but employs the directions of choice approach of Artificially Centered Hit-and-Run. A novel strategy for generating feasible warmup points improved both sampling efficiency and mixing. ll-ACHRB shows overall better performance than current strategies to generate feasible flux samples across several models. Furthermore, we demonstrate that a failure to eliminate unfeasible loops greatly affects sample statistics, in particular the correlation structure. Finally, we discuss recommendations for the interpretation of sampling results and possible algorithmic improvements. Availability and implementation: Source code for MATLAB and OCTAVE including examples are freely available for download at http://www.aibn.uq.edu.au/cssb-resources under Software. Optimization runs can use Gurobi Optimizer (by default if available) or GLPK (included with the algorithm). Contact: lars.nielsen@uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
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    Topics: Biology , Computer Science , Medicine
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  • 2
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    A Survey of General-Purpose Crowdsourcing Techniques (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Since Jeff Howe introduced the term Crowdsourcing in 2006, this human-powered problem-solving paradigm has gained a lot of attention and has been a hot research topic in the field of computer science. Even though a lot of work has been conducted on this topic, so far we do not have a comprehensive survey on most relevant work done in the crowdsourcing field. In this paper, we aim to offer an overall picture of the current state of the art techniques in general-purpose crowdsourcing. According to their focus, we divide this work into three parts, which are: incentive design, task assignment, and quality control. For each part, we start with different problems faced in that area followed by a brief description of existing work and a discussion of pros and cons. In addition, we also present a real scenario on how the different techniques are used in implementing a location-based crowdsourcing platform, gMission. Finally, we highlight the limitations of the current general-purpose crowdsourcing techniques and present some open problems in this area.
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  • 3
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    Crowdsourced Data Management: A Survey (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Any important data management and analytics tasks cannot be completely addressed by automated processes. These tasks, such as entity resolution, sentiment analysis, and image recognition can be enhanced through the use of human cognitive ability. Crowdsouring platforms are an effective way to harness the capabilities of people (i.e., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry. We identify three important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality; (2) Cost Control: The crowd is not free, and cost control aims to reduce the monetary cost; (3) Latency Control: The human workers can be slow, particularly compared to automated computing time scales, so latency-control techniques are required. There has been significant work addressing these three factors for designing crowdsourced tasks, developing crowdsourced data manipulation operators, and optimizing plans consisting of multiple operators. In this paper, we survey and synthesize a wide spectrum of existing studies on crowdsourced data management. Based on this analysis we then outline key factors that need to be considered to improve crowdsourced data management.
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  • 4
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    Distributed Anomaly Detection Using Minimum Volume Elliptical Principal Component Analysis (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Principal component analysis and the residual error is an effective anomaly detection technique. In an environment where anomalies are present in the training set, the derived principal components can be skewed by the anomalies. A further aspect of anomaly detection is that data might be distributed across different nodes in a network and their communication to a centralized processing unit is prohibited due to communication cost. Current solutions to distributed anomaly detection rely on a hierarchical network infrastructure to aggregate data or models; however, in this environment, links close to the root of the tree become critical and congested. In this paper, an algorithm is proposed that is more robust in its derivation of the principal components of a training set containing anomalies. A distributed form of the algorithm is then derived where each node in a network can iterate towards the centralized solution by exchanging small matrices with neighboring nodes. Experimental evaluations on both synthetic and real-world data sets demonstrate the superior performance of the proposed approach in comparison to principal component analysis and alternative anomaly detection techniques. In addition, it is shown that in a variety of network infrastructures, the distributed form of the anomaly detection model is able to derive a close approximation of the centralized model.
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  • 5
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    D-ToSS: A Distributed Throwaway Spatial Index Structure for Dynamic Location Data (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Many applications deal with moving object datasets, e.g., mobile phone social networking, scientific simulations, and ride-sharing services. These applications need to handle a tremendous number of spatial objects that continuously move and execute spatial queries to explore their surroundings. To manage such update-heavy workloads, several throwaway index structures have recently been proposed, where a static index is rebuilt periodically from scratch rather than updated incrementally. It has been shown that throwaway indices outperform specialized moving-object indices that maintain location updates incrementally. However, throwaway indices suffer from scalability due to their single-server design and the only distributed throwaway index (D-MOVIES), extension of a centralized approach, does not scale out as the number of servers increases, especially during query processing phase.
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  • 6
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    Improving Collaborative Recommendation via User-Item Subgroups (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Collaborative filtering (CF) is out of question the most widely adopted and successful recommendation approach. A typical CF-based recommender system associates a user with a group of like-minded users based on their individual preferences over all the items, either explicit or implicit, and then recommends to the user some unobserved items enjoyed by the group. However, we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more reasonable to predict preferences through one user's correlated subgroups, but not the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate a new Multiclass Co-Clustering (MCoC) model, which captures relations of user-to-item, user-to-user, and item-to-item simultaneously. Then, we combine traditional CF algorithms with subgroups for improving their top- $N$ recommendation performance. Our approach can be seen as a new extension of traditional clustering CF models. Systematic experiments on several real data sets have demonstrated the effectiveness of our proposed approach.
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    Conflict-Aware Event-Participant Arrangement and Its Variant for Online Setting (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: With the rapid development of Web 2.0 and Online To Offline (O2O) marketing model, various online e vent- b ased s ocial n etwork s (EBSNs) are getting popular. An important task of EBSNs is to facilitate the most satisfactory event-participant arrangement for both sides, i.e., events enroll more participants and participants are arranged with personally interesting events. Existing approaches usually focus on the arrangement of each single event to a set of potential users, or ignore the conflicts between different events, which leads to infeasible or redundant arrangements. In this paper, to address the shortcomings of existing approaches, we first identify a more general and useful event-participant arrangement problem, called G lobal E vent-participant A rrangement with C onflict and C apacity ( $GEACC$ ) problem, focusing on the conflicts of different events and making event-participant arrangements in a global view. We find that the GEACC problem is NP-hard due to the conflicts among events. Thus, we design two approximation algorithms with provable approximation ratios and an exact algorithm with pruning technique to address this problem. In addition, we propose an online setting of GEACC, called OnlineGEACC, which is also practical in real-world scenarios. We further design an online algorithm with provable performance guarantee. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.
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  • 8
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    K Nearest Neighbour Joins for Big Data on MapReduce: A Theoretical and Experimental Analysis (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Given a point $p$ and a set of points $S$ , the kNN operation finds the $k$ closest points to in $S$ . It is a computational intensive task with a large range of applications such as knowledge discovery or data mining. However, as the volume and the dimension of data increase, only distributed approaches can perform such costly operation in a reasonable time. Recent works have focused on implementing efficient solutions using the MapReduce programming model because it is suitable for distributed large scale data processing. Although these works provide different solutions to the same problem, each one has particular constraints and properties. In this paper, we compare the different existing approaches for computing kNN on MapReduce, first theoretically, and then by performing an extensive experimental evaluation. To be able to compare solutions, we identify three generic steps for kNN computation on MapReduce: data pre-processing, data partitioning, and computation. We then analyze each step from load balancing, accuracy, and complexity aspects. Experiments in this paper use a variety of datasets, and analyze the impact of data volume, data dimension, and the value of k from many perspectives like time and space complexity, and accuracy. The experimental part brings new advantages and shortcomings that are discussed for each algorithm. To the best of our knowledge, this is the first pape- that compares kNN computing methods on MapReduce both theoretically and experimentally with the same setting. Overall, this paper can be used as a guide to tackle kNN-based practical problems in the context of big data.
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  • 9
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    Fast and Accurate Mining the Community Structure: Integrating Center Locating and Membership Optimization (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Mining communities or clusters in networks is valuable in analyzing, designing, and optimizing many natural and engineering complex systems, e.g., protein networks, power grid, and transportation systems. Most of the existing techniques view the community mining problem as an optimization problem based on a given quality function(e.g., modularity), however none of them are grounded with a systematic theory to identify the central nodes in the network. Moreover, how to reconcile the mining efficiency and the community quality still remains an open problem. In this paper, we attempt to address the above challenges by introducing a novel algorithm. First, a kernel function with a tunable influence factor is proposed to measure the leadership of each node, those nodes with highest local leadership can be viewed as the candidate central nodes. Then, we use a discrete-time dynamical system to describe the dynamical assignment of community membership; and formulate the serval conditions to guarantee the convergence of each node's dynamic trajectory, by which the hierarchical community structure of the network can be revealed. The proposed dynamical system is independent of the quality function used, so could also be applied in other community mining models. Our algorithm is highly efficient: the computational complexity analysis shows that the execution time is nearly linearly dependent on the number of nodes in sparse networks. We finally give demonstrative applications of the algorithm to a set of synthetic benchmark networks and also real-world networks to verify the algorithmic performance.
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  • 10
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    Microblogging Content Propagation Modeling Using Topic-Specific Behavioral Factors (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: When a microblogging user adopts some content propagated to her, we can attribute that to three behavioral factors, namely, topic virality , user virality , and user susceptibility . Topic virality measures the degree to which a topic attracts propagations by users. User virality and susceptibility refer to the ability of a user to propagate content to other users, and the propensity of a user adopting content propagated to her, respectively. In this paper, we study the problem of mining these behavioral factors specific to topics from microblogging content propagation data. We first construct a three dimensional tensor for representing the propagation instances. We then propose a tensor factorization framework to simultaneously derive the three sets of behavioral factors. Based on this framework, we develop a numerical factorization model and another probabilistic factorization variant. We also develop an efficient algorithm for the models’ parameters learning. Our experiments on a large Twitter dataset and synthetic datasets show that the proposed models can effectively mine the topic-specific behavioral factors of users and tweet topics. We further demonstrate that the proposed models consistently outperforms the other state-of-the-art content based models in retweet prediction over time.
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  • 11
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    ATD: Anomalous Topic Discovery in High Dimensional Discrete Data (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data. Unlike most anomaly detection (AD) methods, which detect individual anomalies, our proposed method detects groups ( clusters ) of anomalies; i.e., sets of points which collectively exhibit abnormal patterns. In many applications, this can lead to a better understanding of the nature of the atypical behavior and to identifying the sources of the anomalies. Moreover, we consider the case where the atypical patterns exhibit on only a small (salient) subset of the very high dimensional feature space. Individual AD techniques and techniques that detect anomalies using all the features typically fail to detect such anomalies, but our method can detect such instances collectively, discover the shared anomalous patterns exhibited by them, and identify the subsets of salient features. In this paper, we focus on detecting anomalous topics in a batch of text documents, developing our algorithm based on topic models. Results of our experiments show that our method can accurately detect anomalous topics and salient features (words) under each such topic in a synthetic data set and two real-world text corpora and achieves better performance compared to both standard group AD and individual AD techniques. All required code to reproduce our experiments is available from https://github.com/hsoleimani/ATD .
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  • 12
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    Multilabel Classification via Co-Evolutionary Multilabel Hypernetwork (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn an integrated classification model for all labels. The proposed Co-MLHN exploits arbitrary order label correlations and has linear computational complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against state-of-the-art multilabel learning algorithms, in terms of both classification performance and scalability with respect to the number of labels.
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  • 13
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    Prefix-Adaptive and Time-Sensitive Personalized Query Auto Completion (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Query auto completion (QAC) methods recommend queries to search engine users when they start entering a query. Current QAC methods mostly rank query completions based on their past popularity, i.e., on the number of times they have previously been submitted as a query. However, query popularity changes over time and may vary drastically across users. Accordingly, the ranking of query completions should be adjusted. Previous time-sensitive and user-specific QAC methods have been developed separately, yielding significant improvements over methods that are neither time-sensitive nor personalized. We propose a hybrid QAC method that is both time-sensitive and personalized. We extend it to handle long-tail prefixes, which we achieve by assigning optimal weights to the contribution from time-sensitivity and personalization. Using real-world search log datasets, we return top $N$ query suggestions ranked by predicted popularity as estimated from popularity trends and cyclic popularity behavior; we rerank them by integrating similarities to a user's previous queries (both in the current session and in previous sessions). Our method outperforms state-of-the-art time-sensitive QAC baselines, achieving total improvements of between 3 and 7 percent in terms of mean reciprocal rank (MRR). After optimizing the weights, our extended model achieves MRR improvements of between 4 and 8 percent.
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  • 14
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    MAVE: Multilevel wrApper Verification systEm (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Wrappers are pieces of software used to extract data from websites and structure them for further application processing. Unfortunately, websites are continuously evolving and structural changes happen with no forewarning, which usually results in wrappers working incorrectly. Thus, wrappers maintenance is necessary for detecting whether wrapper is extracting erroneous data. The solution consists of using verification models to detect whether wrapper output is statistically similar to the output produced by the wrapper itself when it was successfully invoked in the past. Current proposals present some weaknesses, as the data used to build these models are supposed to be homogeneous, independent, or representative enough, or following a single predefined mathematical model. In this paper, we present MAVE, a novel multilevel wrapper verification system that is based on one-class classification techniques to overcome previous weaknesses. The experimental results show that our proposal outperforms accuracy of current solutions.
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  • 15
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    On genomic repeats and reproducibility (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Results: Here, we present a comprehensive analysis on the reproducibility of computational characterization of genomic variants using high throughput sequencing data. We reanalyzed the same datasets twice, using the same tools with the same parameters, where we only altered the order of reads in the input (i.e. FASTQ file). Reshuffling caused the reads from repetitive regions being mapped to different locations in the second alignment, and we observed similar results when we only applied a scatter/gather approach for read mapping—without prior shuffling. Our results show that, some of the most common variation discovery algorithms do not handle the ambiguous read mappings accurately when random locations are selected. In addition, we also observed that even when the exact same alignment is used, the GATK HaplotypeCaller generates slightly different call sets, which we pinpoint to the variant filtration step. We conclude that, algorithms at each step of genomic variation discovery and characterization need to treat ambiguous mappings in a deterministic fashion to ensure full replication of results. Availability and Implementation: Code, scripts and the generated VCF files are available at DOI:10.5281/zenodo.32611. Contact: calkan@cs.bilkent.edu.tr Supplementary information: Supplementary data are available at Bioinformatics online.
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    Topics: Biology , Computer Science , Medicine
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  • 16
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    The spatiotemporal order of signaling events unveils the logic of development signaling (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: Animals from worms and insects to birds and mammals show distinct body plans; however, the embryonic development of diverse body plans with tissues and organs within is controlled by a surprisingly few signaling pathways. It is well recognized that combinatorial use of and dynamic interactions among signaling pathways follow specific logic to control complex and accurate developmental signaling and patterning, but it remains elusive what such logic is, or even, what it looks like. Results: We have developed a computational model for Drosophila eye development with innovated methods to reveal how interactions among multiple pathways control the dynamically generated hexagonal array of R8 cells. We obtained two novel findings. First, the coupling between the long-range inductive signals produced by the proneural Hh signaling and the short-range restrictive signals produced by the antineural Notch and EGFR signaling is essential for generating accurately spaced R8s. Second, the spatiotemporal orders of key signaling events reveal a robust pattern of lateral inhibition conducted by Ato-coordinated Notch and EGFR signaling to collectively determine R8 patterning. This pattern, stipulating the orders of signaling and comparable to the protocols of communication, may help decipher the well-appreciated but poorly defined logic of developmental signaling. Availability and implementation: The model is available upon request. Contact: hao.zhu@ymail.com Supplementary information: Supplementary data are available at Bioinformatics online.
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    GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: The growing amount of regulatory data from the ENCODE, Roadmap Epigenomics and other consortia provides a wealth of opportunities to investigate the functional impact of single nucleotide polymorphisms (SNPs). Yet, given the large number of regulatory datasets, researchers are posed with a challenge of how to efficiently utilize them to interpret the functional impact of SNP sets. Results: We developed the GenomeRunner web server to automate systematic statistical analysis of SNP sets within a regulatory context. Besides defining the functional impact of SNP sets, GenomeRunner implements novel regulatory similarity/differential analyses, and cell type-specific regulatory enrichment analysis. Validated against literature- and disease ontology-based approaches, analysis of 39 disease/trait-associated SNP sets demonstrated that the functional impact of SNP sets corresponds to known disease relationships. We identified a group of autoimmune diseases with SNPs distinctly enriched in the enhancers of T helper cell subpopulations, and demonstrated relevant cell type-specificity of the functional impact of other SNP sets. In summary, we show how systematic analysis of genomic data within a regulatory context can help interpreting the functional impact of SNP sets. Availability and Implementation: GenomeRunner web server is freely available at http://www.integrativegenomics.org/ . Contact: mikhail.dozmorov@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.
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    Drug-induced adverse events prediction with the LINCS L1000 data (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: Adverse drug reactions (ADRs) are a central consideration during drug development. Here we present a machine learning classifier to prioritize ADRs for approved drugs and pre-clinical small-molecule compounds by combining chemical structure (CS) and gene expression (GE) features. The GE data is from the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset that measured changes in GE before and after treatment of human cells with over 20 000 small-molecule compounds including most of the FDA-approved drugs. Using various benchmarking methods, we show that the integration of GE data with the CS of the drugs can significantly improve the predictability of ADRs. Moreover, transforming GE features to enrichment vectors of biological terms further improves the predictive capability of the classifiers. The most predictive biological-term features can assist in understanding the drug mechanisms of action. Finally, we applied the classifier to all 〉20 000 small-molecules profiled, and developed a web portal for browsing and searching predictive small-molecule/ADR connections. Availability and Implementation: The interface for the adverse event predictions for the 〉20 000 LINCS compounds is available at http://maayanlab.net/SEP-L1000/ . Contact: avi.maayan@mssm.edu Supplementary information : Supplementary data are available at Bioinformatics online.
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    ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: Environmental dissemination of antibiotic resistance genes (ARGs) has become an increasing concern for public health. Metagenomics approaches can effectively detect broad profiles of ARGs in environmental samples; however, the detection and subsequent classification of ARG-like sequences are time consuming and have been severe obstacles in employing metagenomic methods. We sought to accelerate quantification of ARGs in metagenomic data from environmental samples. Results: A Structured ARG reference database (SARG) was constructed by integrating ARDB and CARD, the two most commonly used databases. SARG was curated to remove redundant sequences and optimized to facilitate query sequence identification by similarity. A database with a hierarchical structure (type-subtype-reference sequence) was then constructed to facilitate classification (assigning ARG-like sequence to type, subtype and reference sequence) of sequences identified through similarity search. Utilizing SARG and a previously proposed hybrid functional gene annotation pipeline, we developed an online pipeline called ARGs-OAP for fast annotation and classification of ARG-like sequences from metagenomic data. We also evaluated and proposed a set of criteria important for efficiently conducting metagenomic analysis of ARGs using ARGs-OAP. Availability and Implementation: Perl script for ARGs-OAP can be downloaded from https://github.com/biofuture/Ublastx_stageone . ARGs-OAP can be accessed through http://smile.hku.hk/SARGs . Contact: zhangt@hku.hk or tiedjej@msu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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    chromPlot: visualization of genomic data in chromosomal context (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: : Visualizing genomic data in chromosomal context can help detecting errors in data processing and may suggest new hypotheses to be tested. Here, we report a new tool for displaying large and diverse genomic data along chromosomes. The software is implemented in R so that visualization can be easily integrated with its numerous packages for processing genomic data. It supports simultaneous visualization of multiple tracks of data. Large genomic regions such as QTLs or synteny tracts may be shown along histograms of number of genes, genetic variants, or any other type of genomic element. Tracks can also contain values for continuous or categorical variables and the user can choose among points, connected lines, colored segments, or histograms for representing data. chromPlot takes data from tables in data.frame in GRanges formats. The information necessary to draw chromosomes for mouse and human is included with the package. For other organisms, chromPlot can read Gap and cytoBandIdeo tables from the UCSC Genome Browser. We present common use cases here, and a full tutorial is included as the package’s vignette. Availability and Implementation: chromPlot is distributed under a GLP2 licence at http://www.bioconductor.org . Contact: raverdugo@u.uchile.cl Supplementary information: Supplementary data are available at Bioinformatics online.
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    BLESS 2: accurate, memory-efficient and fast error correction method (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: : The most important features of error correction tools for sequencing data are accuracy, memory efficiency and fast runtime. The previous version of BLESS was highly memory-efficient and accurate, but it was too slow to handle reads from large genomes. We have developed a new version of BLESS to improve runtime and accuracy while maintaining a small memory usage. The new version, called BLESS 2, has an error correction algorithm that is more accurate than BLESS, and the algorithm has been parallelized using hybrid MPI and OpenMP programming. BLESS 2 was compared with five top-performing tools, and it was found to be the fastest when it was executed on two computing nodes using MPI, with each node containing twelve cores. Also, BLESS 2 showed at least 11% higher gain while retaining the memory efficiency of the previous version for large genomes. Availability and implementation: Freely available at https://sourceforge.net/projects/bless-ec Contact: dchen@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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    Deep models for brain EM image segmentation: novel insights and improved performance (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: Accurate segmentation of brain electron microscopy (EM) images is a critical step in dense circuit reconstruction. Although deep neural networks (DNNs) have been widely used in a number of applications in computer vision, most of these models that proved to be effective on image classification tasks cannot be applied directly to EM image segmentation, due to the different objectives of these tasks. As a result, it is desirable to develop an optimized architecture that uses the full power of DNNs and tailored specifically for EM image segmentation. Results: In this work, we proposed a novel design of DNNs for this task. We trained a pixel classifier that operates on raw pixel intensities with no preprocessing to generate probability values for each pixel being a membrane or not. Although the use of neural networks in image segmentation is not completely new, we developed novel insights and model architectures that allow us to achieve superior performance on EM image segmentation tasks. Our submission based on these insights to the 2D EM Image Segmentation Challenge achieved the best performance consistently across all the three evaluation metrics. This challenge is still ongoing and the results in this paper are as of June 5, 2015. Availability and Implementation : https://github.com/ahmed-fakhry/dive Contact : sji@eecs.wsu.edu
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    HilbertCurve: an R/Bioconductor package for high-resolution visualization of genomic data (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: : Hilbert curves enable high-resolution visualization of genomic data on a chromosome- or genome-wide scale. Here we present the HilbertCurve package that provides an easy-to-use interface for mapping genomic data to Hilbert curves. The package transforms the curve as a virtual axis, thereby hiding the details of the curve construction from the user. HilbertCurve supports multiple-layer overlay that makes it a powerful tool to correlate the spatial distribution of multiple feature types. Availability and implementation: The HilbertCurve package and documentation are freely available from the Bioconductor project: http://www.bioconductor.org/packages/devel/bioc/html/HilbertCurve.html Contact: m.schlesner@dkfz.de Supplementary information: Supplementary data are available at Bioinformatics online.
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    GSP: a web-based platform for designing genome-specific primers in polyploids (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: The sequences among subgenomes in a polyploid species have high similarity, making it difficult to design genome-specific primers for sequence analysis. Results: We present GSP, a web-based platform to design genome-specific primers that distinguish subgenome sequences in a polyploid genome. GSP uses BLAST to extract homeologous sequences of the subgenomes in existing databases, performs a multiple sequence alignment, and design primers based on sequence variants in the alignment. An interactive primers diagram, a sequence alignment viewer and a virtual electrophoresis are displayed as parts of the primer design result. GSP also designs specific primers from multiple sequences uploaded by users. Availability and implementation: GSP is a user-friendly and efficient web platform freely accessible at http://probes.pw.usda.gov/GSP . Source code and command-line application are available at https://github.com/bioinfogenome/GSP . Contacts: yong.gu@ars.usda.gov or devin.coleman-derr@ars.usda.gov Supplementary information: Supplementary data are available at Bioinformatics online.
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    FRODOCK 2.0: fast protein-protein docking server (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: : The prediction of protein–protein complexes from the structures of unbound components is a challenging and powerful strategy to decipher the mechanism of many essential biological processes. We present a user-friendly protein–protein docking server based on an improved version of FRODOCK that includes a complementary knowledge-based potential. The web interface provides a very effective tool to explore and select protein–protein models and interactively screen them against experimental distance constraints. The competitive success rates and efficiency achieved allow the retrieval of reliable potential protein–protein binding conformations that can be further refined with more computationally demanding strategies. Availability and Implementation: The server is free and open to all users with no login requirement at http://frodock.chaconlab.org Contact: pablo@chaconlab.org Supplementary information: Supplementary data are available at Bioinformatics online.
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    XIBD: software for inferring pairwise identity by descent on the X chromosome (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: : XIBD performs pairwise relatedness mapping on the X chromosome using dense single nucleotide polymorphism (SNP) data from either SNP chips or next generation sequencing data. It correctly accounts for the difference in chromosomal numbers between males and females and estimates global relatedness as well as regions of the genome that are identical by descent (IBD). XIBD also generates novel graphical summaries of all pairwise IBD tracts for a cohort making it very useful for disease locus mapping. Availability and implementation: XIBD is written in R/Rcpp and executed from shell scripts that are freely available from http://bioinf.wehi.edu.au/software/XIBD along with accompanying reference datasets. Contact: henden.l@wehi.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
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    FREGAT: an R package for region-based association analysis (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: : Several approaches to the region-based association analysis of quantitative traits have recently been developed and successively applied. However, no software package has been developed that implements all of these approaches for either independent or structured samples. Here we introduce FREGAT (Family REGional Association Tests), an R package that can handle family and population samples and implements a wide range of region-based association methods including burden tests, functional linear models, and kernel machine-based regression. FREGAT can be used in genome/exome-wide region-based association studies of quantitative traits and candidate gene analysis. FREGAT offers many useful options to empower its users and increase the effectiveness and applicability of region-based association analysis. Availability and Implementation: https://cran.r-project.org/web/packages/FREGAT/index.html Supplementary Information: Supplementary data are available at Bioinformatics Online. Contact: belon@bionet.nsc.ru
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    AlgoRun: a Docker-based packaging system for platform-agnostic implemented algorithms (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: There is a growing need in bioinformatics for easy-to-use software implementations of algorithms that are usable across platforms. At the same time, reproducibility of computational results is critical and often a challenge due to source code changes over time and dependencies. Results: The approach introduced in this paper addresses both of these needs with AlgoRun, a dedicated packaging system for implemented algorithms, using Docker technology. Implemented algorithms, packaged with AlgoRun, can be executed through a user-friendly interface directly from a web browser or via a standardized RESTful web API to allow easy integration into more complex workflows. The packaged algorithm includes the entire software execution environment, thereby eliminating the common problem of software dependencies and the irreproducibility of computations over time. AlgoRun-packaged algorithms can be published on http://algorun.org , a centralized searchable directory to find existing AlgoRun-packaged algorithms. Availability and implementation: AlgoRun is available at http://algorun.org and the source code under GPL license is available at https://github.com/algorun Contact: laubenbacher@uchc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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    Robust Median Reversion Strategy for Online Portfolio Selection (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Online portfolio selection has attracted increasing attention from data mining and machine learning communities in recent years. An important theory in financial markets is mean reversion, which plays a critical role in some state-of-the-art portfolio selection strategies. Although existing mean reversion strategies have been shown to achieve good empirical performance on certain datasets, they seldom carefully deal with noise and outliers in the data, leading to suboptimal portfolios, and consequently yielding poor performance in practice. In this paper, we propose to exploit the reversion phenomenon by using robust $L_1$ -median estimators, and design a novel online portfolio selection strategy named “Robust Median Reversion” (RMR), which constructs optimal portfolios based on the improved reversion estimator. We examine the performance of the proposed algorithms on various real markets with extensive experiments. Empirical results show that RMR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Finally, RMR runs in linear time, and thus is suitable for large-scale real-time algorithmic trading applications.
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    SPIRIT: A Tree Kernel-Based Method for Topic Person Interaction Detection (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: The development of a topic in a set of topic documents is constituted by a series of person interactions at a specific time and place. Knowing the interactions of the persons mentioned in these documents is helpful for readers to better comprehend the documents. In this paper, we propose a topic person interaction detection method called SPIRIT, which classifies the text segments in a set of topic documents that convey person interactions. We design the rich interactive tree structure to represent syntactic, context, and semantic information of text, and this structure is incorporated into a tree-based convolution kernel to identify interactive segments. Experiment results based on real world topics demonstrate that the proposed rich interactive tree structure effectively detects the topic person interactions and that our method outperforms many well-known relation extraction and protein-protein interaction methods.
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    User Preference Learning for Online Social Recommendation (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: A social recommendation system has attracted a lot of attention recently in the research communities of information retrieval, machine learning, and data mining. Traditional social recommendation algorithms are often based on batch machine learning methods which suffer from several critical limitations, e.g., extremely expensive model retraining cost whenever new user ratings arrive, unable to capture the change of user preferences over time. Therefore, it is important to make social recommendation system suitable for real-world online applications where data often arrives sequentially and user preferences may change dynamically and rapidly. In this paper, we present a new framework of online social recommendation from the viewpoint of online graph regularized user preference learning (OGRPL), which incorporates both collaborative user-item relationship as well as item content features into an unified preference learning process. We further develop an efficient iterative procedure, OGRPL-FW which utilizes the Frank-Wolfe algorithm, to solve the proposed online optimization problem. We conduct extensive experiments on several large-scale datasets, in which the encouraging results demonstrate that the proposed algorithms obtain significantly lower errors (in terms of both RMSE and MAE) than the state-of-the-art online recommendation methods when receiving the same amount of training data in the online learning process.
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    Private Over-Threshold Aggregation Protocols over Distributed Datasets (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: In this paper, we revisit the private over-threshold data aggregation problem. We formally define the problem's security requirements as both data and user privacy goals. To achieve both goals, and to strike a balance between efficiency and functionality, we devise an efficient cryptographic construction and its proxy-based variant. Both schemes are provably secure in the semi-honest model. Our key idea for the constructions and their malicious variants is to compose two encryption functions tightly coupled in a way that the two functions are commutative and one public-key encryption has an additive homomorphism. We call that double encryption. We analyze the computational and communication complexities of our construction, and show that it is much more efficient than the existing protocols in the literature. Specifically, our protocol has linear complexity in computation and communication with respect to the number of users. Its round complexity is also linear in the number of users. Finally, we show that our basic protocol is efficiently transformed into a stronger protocol secure in the presence of malicious adversaries, and provide the resulting protocol's performance and security analysis.
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    Mining Health Examination Records—A Graph-Based Approach (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: General health examination is an integral part of healthcare in many countries. Identifying the participants at risk is important for early warning and preventive intervention. The fundamental challenge of learning a classification model for risk prediction lies in the unlabeled data that constitutes the majority of the collected dataset. Particularly, the unlabeled data describes the participants in health examinations whose health conditions can vary greatly from healthy to very-ill. There is no ground truth for differentiating their states of health. In this paper, we propose a graph-based, semi-supervised learning algorithm called SHG-Health (Semi-supervised Heterogeneous Graph on Health) for risk predictions to classify a progressively developing situation with the majority of the data unlabeled. An efficient iterative algorithm is designed and the proof of convergence is given. Extensive experiments based on both real health examination datasets and synthetic datasets are performed to show the effectiveness and efficiency of our method.
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    Toward Optimal Feature Selection in Naive Bayes for Text Categorization (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-08-05
    Description: Automated feature selection is important for text categorization to reduce feature size and to speed up learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity for classification. We first revisit two information measures: Kullback-Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their asymptotic properties relating to type I and type II errors of a Bayesian classifier. We then introduce a new divergence measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure multi-distribution divergence for multi-class classification. Based on the JMH-divergence, we develop two efficient feature selection methods, termed maximum discrimination ( $MD$ ) and methods, for text categorization. The promising results of extensive experiments demonstrate the effectiveness of the proposed approaches.
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    A General Multi-Context Embedding Model for Mining Human Trajectory Data (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: The proliferation of location-based social networks, such as Foursquare and Facebook Places, offers a variety of ways to record human mobility, including user generated geo-tagged contents, check-in services, and mobile apps. Although trajectory data is of great value to many applications, it is challenging to analyze and mine trajectory data due to the complex characteristics reflected in human mobility, which is affected by multiple contextual information. In this paper, we propose a Multi-Context Trajectory Embedding Model, called MC-TEM, to explore contexts in a systematic way. MC-TEM is developed in the distributed representation learning framework, and it is flexible to characterize various kinds of useful contexts for different applications. To the best of our knowledge, it is the first time that the distributed representation learning methods apply to trajectory data. We formally incorporate multiple context information of trajectory data into the proposed model, including user-level, trajectory-level, location-level, and temporal contexts. All the context information is represented in the same embedding space. We apply MC-TEM to two challenging tasks, namely location recommendation and social link prediction. We conduct extensive experiments on three real-world datasets. Extensive experiment results have demonstrated the superiority of our MC-TEM model over several state-of-the-art methods.
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    Access Time Oracle for Planar Graphs (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: The study of urban networks reveals that the accessibility of important city objects for the vehicle traffic and pedestrians is significantly correlated to the popularity, micro-criminality, micro-economic vitality, and social liveability of the city, and is always the chief factor in regulating the growth and expansion of the city. The accessibility between different components of an urban structure are frequently measured along the streets and routes considered as edges of a planar graph, while the traffic ultimate destination points and street junctions are treated as vertices. For estimation of the accessibility of destination vertex $j$ from vertex $i$ through urban networks, in particular, the random walks are used to calculate the expected distance a random walker starting from $i$ makes before $j$ is visited (known as access time ). The state-of-the-art of access time computation is costly in large planar graphs since it involves matrix operation over entire graph. The time complexity is $O(n^{2.376})$ where $n$ is the number of vertices in the planar graph. To enable efficient access time query answering in large planar graphs, this work proposes the first access time oracle which is based on the proposed access time decomposition and reconstruction scheme. The oracle is a hierarchical data structure with deliberate design on the relationships between different hierarchical levels. The storage requirement of the proposed oracle is $O(n^{frac{4}{3}}log log n)$ and the access time query response time is $O(n^{frac{2}{3}})$ . The extensive tests on a number of large real-world road networks (with up to about 2 million vertices) have verified the superiority of the proposed oracle.
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    Automatic Clustering via Outward Statistical Testing on Density Metrics (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Clustering is one of the research hotspots in the field of data mining and has extensive applications in practice. Recently, Rodriguez and Laio [1] published a clustering algorithm on Science that identifies the clustering centers in an intuitive way and clusters objects efficiently and effectively. However, the algorithm is sensitive to a preassigned parameter and suffers from the identification of the “ideal” number of clusters. To overcome these shortages, this paper proposes a new clustering algorithm that can detect the clustering centers automatically via statistical testing. Specifically, the proposed algorithm first defines a new metric to measure the density of an object that is more robust to the preassigned parameter, further generates a metric to evaluate the centrality of each object. Afterwards, it identifies the objects with extremely large centrality metrics as the clustering centers via an outward statistical testing method. Finally, it groups the remaining objects into clusters containing their nearest neighbors with higher density. Extensive experiments are conducted over different kinds of clustering data sets to evaluate the performance of the proposed algorithm and compare with the algorithm in Science. The results show the effectiveness and robustness of the proposed algorithm.
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    Discovering the $k$ Representative Skyline Over a Sliding Window (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: A representative skyline contains $k$ skyline points that can represent its corresponding full skyline. The existing measuring criteria of $k$ representative skylines are specifically designed for static data, and they cannot effectively handle streaming data. In this paper, we focus on the problem of calculating the $k$ representative skyline over data streams. First, we propose a new criterion to choose $k$ skyline points as the $k$ representative skyline for data stream environments, termed the $k$ largest dominance skyline ( $k$ -LDS), which is representative to the entire data set and is highly stable over the streaming data. Second, we propose an efficient exact algorithm, called Prefix-based Algorithm (PBA), to solve the $k$ -LDS problem in a 2-dimensional space. The time complexity of PBA is only $mathcal {O}((M-k)times k)$ where $M$ is the size of the full skyline set. Third, the $k$ -LDS problem for a $d$ -dimensional ( $dge 3$ ) space turns out to be very complex. Therefore, a greedy algorithm is designed to answer $k$ -LDS queries. To further accelerate the calculation, we propose a $epsilon$ -greedy algorithm which can achieve an approximate factor of $frac{1}{(1+epsilon)}(1-frac{1}{sqrt{e}})$
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    Deep Learning of Transferable Representation for Scalable Domain Adaptation (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Domain adaptation generalizes a learning model across source domain and target domain that are sampled from different distributions. It is widely applied to cross-domain data mining for reusing labeled information and mitigating labeling consumption. Recent studies reveal that deep neural networks can learn abstract feature representation, which can reduce, but not remove, the cross-domain discrepancy. To enhance the invariance of deep representation and make it more transferable across domains, we propose a unified deep adaptation framework for jointly learning transferable representation and classifier to enable scalable domain adaptation, by taking the advantages of both deep learning and optimal two-sample matching. The framework constitutes two inter-dependent paradigms, unsupervised pre-training for effective training of deep models using deep denoising autoencoders, and supervised fine-tuning for effective exploitation of discriminative information using deep neural networks, both learned by embedding the deep representations to reproducing kernel Hilbert spaces (RKHSs) and optimally matching different domain distributions. To enable scalable learning, we develop a linear-time algorithm using unbiased estimate that scales linearly to large samples. Extensive empirical results show that the proposed framework significantly outperforms state of the art methods on diverse adaptation tasks: sentiment polarity prediction, email spam filtering, newsgroup content categorization, and visual object recognition.
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    Contextual Operation for Recommender Systems (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: With the rapid growth of various applications on the Internet, recommender systems become fundamental for helping users alleviate the problem of information overload. Since contextual information is a significant factor in modeling the user behavior, various context-aware recommendation methods have been proposed recently. The state-of-the-art context modeling methods usually treat contexts as certain dimensions similar to those of users and items, and capture relevances between contexts and users/items. However, such kind of relevance has much difficulty in explanation. Some works on multi-domain relation prediction can also be used for the context-aware recommendation, but they have limitations in generating recommendations under a large amount of contextual information. Motivated by recent works in natural language processing, we represent each context value with a latent vector, and model the contextual information as a semantic operation on the user and item. Besides, we use the contextual operating tensor to capture the common semantic effects of contexts. Experimental results show that the proposed Context Operating Tensor (COT) model yields significant improvements over the competitive compared methods on three typical datasets. From the experimental results of COT, we also obtain some interesting observations which follow our intuition.
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    Conflicts to Harmony: A Framework for Resolving Conflicts in Heterogeneous Data by Truth Discovery (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: In many applications, one can obtain descriptions about the same objects or events from a variety of sources. As a result, this will inevitably lead to data or information conflicts. One important problem is to identify the true information (i.e., the truths ) among conflicting sources of data. It is intuitive to trust reliable sources more when deriving the truths, but it is usually unknown which one is more reliable a priori . Moreover, each source possesses a variety of properties with different data types. An accurate estimation of source reliability has to be made by modeling multiple properties in a unified model. Existing conflict resolution work either does not conduct source reliability estimation, or models multiple properties separately. In this paper, we propose to resolve conflicts among multiple sources of heterogeneous data types. We model the problem using an optimization framework where truths and source reliability are defined as two sets of unknown variables. The objective is to minimize the overall weighted deviation between the truths and the multi-source observations where each source is weighted by its reliability. Different loss functions can be incorporated into this framework to recognize the characteristics of various data types, and efficient computation approaches are developed. The proposed framework is further adapted to deal with streaming data in an incremental fashion and large-scale data in MapReduce model. Experiments on real-world weather, stock, and flight data as well as simulated multi-source data demonstrate the advantage of jointly modeling different data types in the proposed framework.
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    GenPerm: A Unified Method for Detecting Non-Overlapping and Overlapping Communities (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Detection of non-overlapping and overlapping communities are essentially the same problem. However, current algorithms focus either on finding overlapping or non-overlapping communities. We present a generalized framework that can identify both non-overlapping and overlapping communities, without any prior input about the network or its community distribution. To do so, we introduce a vertex-based metric, GenPerm , that quantifies by how much a vertex belongs to each of its constituent communities. Our community detection algorithm is based on maximizing the GenPerm over all the vertices in the network. We demonstrate, through experiments over synthetic and real-world networks, that GenPerm is more effective than other metrics in evaluating community structure. Further, we show that due to its vertex-centric property, GenPerm can be used to unfold several inferences beyond community detection, such as core-periphery analysis and message spreading. Our algorithm for maximizing GenPerm outperforms six state-of-the-art algorithms in accurately predicting the ground-truth labels. Finally, we discuss the problem of resolution limit in overlapping communities and demonstrate that maximizing GenPerm can mitigate this problem.
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    Exploring Hierarchies in Online Social Networks (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Social hierarchy (i.e., pyramid structure of societies) is a fundamental concept in sociology and social network analysis. The importance of social hierarchy in a social network is that the topological structure of the social hierarchy is essential in both shaping the nature of social interactions between individuals and unfolding the structure of the social networks. The social hierarchy found in a social network can be utilized to improve the accuracy of link prediction, provide better query results, rank web pages, and study information flow and spread in complex networks. In this paper, we model a social network as a directed graph $G$ , and consider the social hierarchy as DAG (directed acyclic graph) of $G$ , denoted as $G_D$ . By DAG, all the vertices in $G$ can be partitioned into different levels, the vertices at the same level represent a disjoint group in the social hierarchy, and all the edges in DAG follow one direction. The main issue we study in this paper is how to find DAG $G_D$ in $G$ . The approach we take is to find $G_D$ by removing all possible cycles from $G$ such that $G = {cal U}(G) cup G_D$ , where ${cal U}(G)$ is a maximum Eulerian subgraph which contains all possible cycles. We give the reasons for doing so, investigate the properties of $G_D$ found, and discuss the applications. In addition, we develop a novel two-phase algorithm, called Greedy-&-Refine, which greedily computes an Eulerian subgraph and then refines this greedy solution to find the maximum Eulerian subgraph. We give a bound between the greedy solution and the optimal. The quality of our greedy approach is high. We conduct comprehensive experimental studies over 14 real-world datasets. The results show that our algorithms are at least two orders of magnitude faster than the baseline algorithm.
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    Interactive Visualization of Large Data Sets (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Visualization provides a powerful means for data analysis. But to be practical, visual analytics tools must support smooth and flexible use of visualizations at a fast rate. This becomes increasingly onerous with the ever-increasing size of real-world datasets. First, large databases make interaction more difficult once query response time exceeds several seconds. Second, any attempt to show all data points will overload the visualization, resulting in chaos that will only confuse the user. Over the last few years, substantial effort has been put into addressing both of these issues and many innovative solutions have been proposed. Indeed, data visualization is a topic that is too large to be addressed in a single survey paper. Thus, we restrict our attention here to interactive visualization of large data sets. Our focus then is skewed in a natural way towards query processing problem—provided by an underlying database system—rather than to the actual data visualization problem.
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    Incremental Evolving Domain Adaptation (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Almost all of the existing domain adaptation methods assume that all test data belong to a single stationary target distribution. However, in many real world applications, data arrive sequentially and the data distribution is continuously evolving. In this paper, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced. We assume that the available data for the source domain are labeled but the examples of the target domain can be unlabeled and arrive sequentially. Moreover, the distribution of the target domain can evolve continuously over time. We propose the Evolving Domain Adaptation (EDA) method that first finds a new feature space in which the source domain and the current target domain are approximately indistinguishable. Therefore, source and target domain data are similarly distributed in the new feature space and we use a semi-supervised classification method to utilize both the unlabeled data of the target domain and the labeled data of the source domain. Since test data arrives sequentially, we propose an incremental approach both for finding the new feature space and for semi-supervised classification. Experiments on several real datasets demonstrate the superiority of our proposed method in comparison to the other recent methods.
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    Quantifying Political Leaning from Tweets, Retweets, and Retweeters (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold.
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    Set Matching Measures for External Cluster Validity (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Comparing two clustering results of a data set is a challenging task in cluster analysis. Many external validity measures have been proposed in the literature. A good measure should be invariant to the changes of data size, cluster size, and number of clusters. We give an overview of existing set matching indexes and analyze their properties. Set matching measures are based on matching clusters from two clusterings. We analyze the measures in three parts: 1) cluster similarity, 2) matching, and 3) overall measurement. Correction for chance is also investigated and we prove that normalized mutual information and variation of information are intrinsically corrected. We propose a new scheme of experiments based on synthetic data for evaluation of an external validity index. Accordingly, popular external indexes are evaluated and compared when applied to clusterings of different data size, cluster size, and number of clusters. The experiments show that set matching measures are clearly better than the other tested. Based on the analytical comparisons, we introduce a new index called Pair Sets Index (PSI).
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    Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algorithms (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Many commercial products and academic research activities are embracing behavior analysis as a technique for improving detection of attacks of many sorts—from retweet boosting, hashtag hijacking to link advertising. Traditional approaches focus on detecting dense blocks in the adjacency matrix of graph data, and recently, the tensors of multimodal data. No method gives a principled way to score the suspiciousness of dense blocks with different numbers of modes and rank them to draw human attention accordingly. In this paper, we first give a list of axioms that any metric of suspiciousness should satisfy; we propose an intuitive, principled metric that satisfies the axioms, and is fast to compute; moreover, we propose CrossSpot , an algorithm to spot dense blocks that are worth inspecting, typically indicating fraud or some other noteworthy deviation from the usual, and sort them in the order of importance (“suspiciousness”). Finally, we apply CrossSpot to the real data, where it improves the F1 score over previous techniques by 68 percent and finds suspicious behavioral patterns in social datasets spanning 0.3 billion posts.
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    Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namely multi-skill spatial crowdsourcing (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers’ benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy, $g$ -divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.
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    TopicSketch: Real-Time Bursty Topic Detection from Twitter (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Twitter has become one of the largest microblogging platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in Twitter is one that triggers a surge of relevant tweets within a short period of time, which often reflects important events of mass interest. How to leverage Twitter for early detection of bursty topics has therefore become an important research problem with immense practical value. Despite the wealth of research work on topic modelling and analysis in Twitter, it remains a challenge to detect bursty topics in real-time. As existing methods can hardly scale to handle the task with the tweet stream in real-time, we propose in this paper $sf {TopicSketch}$ , a sketch-based topic model together with a set of techniques to achieve real-time detection. We evaluate our solution on a tweet stream with over 30 million tweets. Our experiment results show both efficiency and effectiveness of our approach. Especially it is also demonstrated that $sf {TopicSketch}$ on a single machine can potentially handle hundreds of millions tweets per day, which is on the same scale of the total number of daily tweets in Twitter, and present bursty events in finer-granularity.
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    VENUS: A System for Streamlined Graph Computation on a Single PC (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Recent studies show that disk-based graph computation systems on just a single PC can be as highly competitive as cluster-based systems on large-scale problems. Inspired by this remarkable progress, we develop VENUS, a disk-based graph computation system which is able to handle billion-scale graphs efficiently on a commodity PC. VENUS adopts a novel computing architecture that features vertex-centric “streamlined” processing—the graph is sequentially loaded and an update function is executed for each vertex in parallel on the fly. VENUS deliberately avoids loading batch edge data by separating read-only structure data from mutable vertex data on disk, and minimizes random IOs by caching vertex data in the main memory whenever possible. The streamlined processing is realized with efficient sequential scan over massive structure data and fast feeding the update function for a large number of vertices. Extensive evaluation on large real-world and synthetic graphs has demonstrated the efficiency of VENUS. For example, to run the PageRank algorithm on a Twitter graph of 42 million vertices and 1.4 billion edges, Spark needs 8.1 minutes with 50 machines and GraphChi spends 13 minutes using high-speed SSD, while VENUS only takes 5 minutes on one machine with an ordinary hard disk.
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    Counting Triangles in Large Graphs by Random Sampling (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: The problem of counting triangles in graphs has been well studied in the literature. However, all existing algorithms, exact or approximate, spend at least linear time in the size of the graph (except a recent theoretical result), which can be prohibitive on today's large graphs. Nevertheless, we observe that the ideas in many existing triangle counting algorithms can be coupled with random sampling to yield potentially sublinear-time algorithms that return an approximation of the triangle count without looking at the whole graph. This paper makes these random sampling algorithms more explicit, and presents an experimental and analytical comparison of different approaches, identifying the best performers among a number of candidates.
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    Discriminative Link Prediction using Local, Community, and Global Signals (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Predicting plausible links that may emerge between pairs of nodes is an important task in social network analysis, with over a decade of active research. Here, we propose a novel framework for link prediction. It integrates signals from node features, the existing local link neighborhood of a node pair, community-level link density, and global graph properties. Our framework uses a stacked two-level learning paradigm. At the lower level, the first two kinds of features are processed by a novel local learner. Its outputs are then integrated with the last two kinds of features by a conventional discriminative learner at the upper-level. We also propose a new stratified sampling scheme for evaluating link prediction algorithms in the face of an extremely large number of potential edges, out of which very few will ever materialize. It is not tied to a specific application of link prediction, but robust to a range of application requirements. We report on extensive experiments with seven benchmark datasets and over five competitive baseline systems. The system we present consistently shows at least 10 percent accuracy improvement over state-of-the-art, and over 30 percent improvement in some cases. We also demonstrate, through ablation, that our features are complementary in terms of the signals and accuracy benefits they provide.
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    Incremental and Decremental Max-Flow for Online Semi-Supervised Learning (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Max-flow has been adopted for semi-supervised data modelling, yet existing algorithms were derived only for the learning from static data. This paper proposes an online max-flow algorithm for the semi-supervised learning from data streams. Consider a graph learned from labelled and unlabelled data, and the graph being updated dynamically for accommodating online data adding and retiring. In learning from the resulting non stationary graph, we augment and de-augment paths to update max-flow with a theoretical guarantee that the updated max-flow equals to that from batch retraining. For classification, we compute min-cut over current max-flow, so that minimized number of similar sample pairs are classified into distinct classes. Empirical evaluation on real-world data reveals that our algorithm outperforms state-of-the-art stream classification algorithms.
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    Explaining Missing Answers to Top-k SQL Queries (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-07-08
    Description: Due to the fact that existing database systems are increasingly more difficult to use, improving the quality and the usability of database systems has gained tremendous momentum over the last few years. In particular, the feature of explaining why some expected tuples are missing in the result of a query has received more attention. In this paper, we study the problem of explaining missing answers to top-k queries in the context of SQL (i.e., with selection, projection, join, and aggregation). To approach this problem, we use the query-refinement method. That is, given as inputs the original top-k SQL query and a set of missing tuples, our algorithms return to the user a refined query that includes both the missing tuples and the original query results. Case studies and experimental results show that our algorithms are able to return high quality explanations efficiently.
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    Integrated genome browser: visual analytics platform for genomics (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
    Description: Motivation: Genome browsers that support fast navigation through vast datasets and provide interactive visual analytics functions can help scientists achieve deeper insight into biological systems. Toward this end, we developed Integrated Genome Browser (IGB), a highly configurable, interactive and fast open source desktop genome browser. Results: Here we describe multiple updates to IGB, including all-new capabilities to display and interact with data from high-throughput sequencing experiments. To demonstrate, we describe example visualizations and analyses of datasets from RNA-Seq, ChIP-Seq and bisulfite sequencing experiments. Understanding results from genome-scale experiments requires viewing the data in the context of reference genome annotations and other related datasets. To facilitate this, we enhanced IGB’s ability to consume data from diverse sources, including Galaxy, Distributed Annotation and IGB-specific Quickload servers. To support future visualization needs as new genome-scale assays enter wide use, we transformed the IGB codebase into a modular, extensible platform for developers to create and deploy all-new visualizations of genomic data. Availability and implementation: IGB is open source and is freely available from http://bioviz.org/igb . Contact: aloraine@uncc.edu
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    Minimap and miniasm: fast mapping and de novo assembly for noisy long sequences (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
    Description: Motivation: Single Molecule Real-Time (SMRT) sequencing technology and Oxford Nanopore technologies (ONT) produce reads over 10 kb in length, which have enabled high-quality genome assembly at an affordable cost. However, at present, long reads have an error rate as high as 10–15%. Complex and computationally intensive pipelines are required to assemble such reads. Results: We present a new mapper, minimap and a de novo assembler, miniasm, for efficiently mapping and assembling SMRT and ONT reads without an error correction stage. They can often assemble a sequencing run of bacterial data into a single contig in a few minutes, and assemble 45-fold Caenorhabditis elegans data in 9 min, orders of magnitude faster than the existing pipelines, though the consensus sequence error rate is as high as raw reads. We also introduce a pairwise read mapping format and a graphical fragment assembly format, and demonstrate the interoperability between ours and current tools. Availability and implementation: https://github.com/lh3/minimap and https://github.com/lh3/miniasm Contact: hengli@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online.
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    jSplice: a high-performance method for accurate prediction of alternative splicing events and its application to large-scale renal cancer transcriptome data (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
    Description: Motivation: Alternative splicing represents a prime mechanism of post-transcriptional gene regulation whose misregulation is associated with a broad range of human diseases. Despite the vast availability of transcriptome data from different cell types and diseases, bioinformatics-based surveys of alternative splicing patterns remain a major challenge due to limited availability of analytical tools that combine high accuracy and rapidity. Results: We describe here a novel junction-centric method, jSplice, that enables de novo extraction of alternative splicing events from RNA-sequencing data with high accuracy, reliability and speed. Application to clear cell renal carcinoma (ccRCC) cell lines and 65 ccRCC patients revealed experimentally validatable alternative splicing changes and signatures able to prognosticate ccRCC outcome. In the aggregate, our results propose jSplice as a key analytic tool for the derivation of cell context-dependent alternative splicing patterns from large-scale RNA-sequencing datasets. Availability and implementation: jSplice is a standalone Python application freely available at http://www.mhs.biol.ethz.ch/research/krek/jsplice . Contact: wilhelm.krek@biol.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.
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    Beta-Poisson model for single-cell RNA-seq data analyses (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
    Description: Motivation: Single-cell RNA-sequencing technology allows detection of gene expression at the single-cell level. One typical feature of the data is a bimodality in the cellular distribution even for highly expressed genes, primarily caused by a proportion of non-expressing cells. The standard and the over-dispersed gamma-Poisson models that are commonly used in bulk-cell RNA-sequencing are not able to capture this property. Results: We introduce a beta-Poisson mixture model that can capture the bimodality of the single-cell gene expression distribution. We further integrate the model into the generalized linear model framework in order to perform differential expression analyses. The whole analytical procedure is called BPSC. The results from several real single-cell RNA-seq datasets indicate that ~90% of the transcripts are well characterized by the beta-Poisson model; the model-fit from BPSC is better than the fit of the standard gamma-Poisson model in 〉 80% of the transcripts. Moreover, in differential expression analyses of simulated and real datasets, BPSC performs well against edgeR, a conventional method widely used in bulk-cell RNA-sequencing data, and against scde and MAST, two recent methods specifically designed for single-cell RNA-seq data. Availability and Implementation: An R package BPSC for model fitting and differential expression analyses of single-cell RNA-seq data is available under GPL-3 license at https://github.com/nghiavtr/BPSC . Contact: yudi.pawitan@ki.se or mattias.rantalainen@ki.se Supplementary information: Supplementary data are available at Bioinformatics online.
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    APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
    Description: Motivation: Biological network querying is a problem requiring a considerable computational effort to be solved. Given a target and a query network, it aims to find occurrences of the query in the target by considering topological and node similarities (i.e. mismatches between nodes, edges, or node labels). Querying tools that deal with similarities are crucial in biological network analysis because they provide meaningful results also in case of noisy data. In addition, as the size of available networks increases steadily, existing algorithms and tools are becoming unsuitable. This is rising new challenges for the design of more efficient and accurate solutions. Results: This paper presents APPAGATO , a stochastic and parallel algorithm to find approximate occurrences of a query network in biological networks. APPAGATO handles node, edge and node label mismatches. Thanks to its randomic and parallel nature, it applies to large networks and, compared with existing tools, it provides higher performance as well as statistically significant more accurate results. Tests have been performed on protein–protein interaction networks annotated with synthetic and real gene ontology terms. Case studies have been done by querying protein complexes among different species and tissues. Availability and implementation: APPAGATO has been developed on top of CUDA-C ++ Toolkit 7.0 framework. The software is available online http://profs.sci.univr.it/~bombieri/APPAGATO . Contact: rosalba.giugno@univr.it Supplementary information: Supplementary data are available at Bioinformatics online.
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    TreesimJ: a flexible, forward time population genetic simulator (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
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    SharpViSu: integrated analysis and segmentation of super-resolution microscopy data (2016)
    Oxford University Press
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    Publication Date: 2016-07-09
    Description: : We introduce SharpViSu, an interactive open-source software with a graphical user interface, which allows performing processing steps for localization data in an integrated manner. This includes common features and new tools such as correction of chromatic aberrations, drift correction based on iterative cross-correlation calculations, selection of localization events, reconstruction of 2D and 3D datasets in different representations, estimation of resolution by Fourier ring correlation, clustering analysis based on Voronoi diagrams and Ripley’s functions. SharpViSu is optimized to work with eventlist tables exported from most popular localization software. We show applications of these on single and double-labelled super-resolution data. Availability and implementation: SharpViSu is available as open source code and as compiled stand-alone application under https://github.com/andronovl/SharpViSu . Contact: klaholz@igbmc.fr Supplementary information: Supplementary data are available at Bioinformatics online.
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    ecceTERA: comprehensive gene tree-species tree reconciliation using parsimony (2016)
    Oxford University Press
    Details
    Publication Date: 2016-06-25
    Description: : A gene tree-species tree reconciliation explains the evolution of a gene tree within the species tree given a model of gene-family evolution. We describe ecceTERA, a program that implements a generic parsimony reconciliation algorithm, which accounts for gene duplication, loss and transfer (DTL) as well as speciation, involving sampled and unsampled lineages, within undated, fully dated or partially dated species trees. The ecceTERA reconciliation model and algorithm generalize or improve upon most published DTL parsimony algorithms for binary species trees and binary gene trees. Moreover, ecceTERA can estimate accurate species-tree aware gene trees using amalgamation. Availability and implementation : ecceTERA is freely available under http://mbb.univ-montp2.fr/MBB/download_sources/16__ecceTERA and can be run online at http://mbb.univ-montp2.fr/MBB/subsection/softExec.php?soft=eccetera . Contact: celine.scornavacca@umontpellier.fr Supplementary information: Supplementary data are available at Bioinformatics online.
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    NMRPro: an integrated web component for interactive processing and visualization of NMR spectra (2016)
    Oxford University Press
    Details
    Publication Date: 2016-06-25
    Description: : The popularity of using NMR spectroscopy in metabolomics and natural products has driven the development of an array of NMR spectral analysis tools and databases. Particularly, web applications are well used recently because they are platform-independent and easy to extend through reusable web components. Currently available web applications provide the analysis of NMR spectra. However, they still lack the necessary processing and interactive visualization functionalities. To overcome these limitations, we present NMRPro, a web component that can be easily incorporated into current web applications, enabling easy-to-use online interactive processing and visualization. NMRPro integrates server-side processing with client-side interactive visualization through three parts: a python package to efficiently process large NMR datasets on the server-side, a Django App managing server-client interaction, and SpecdrawJS for client-side interactive visualization. Availability and implementation: Demo and installation instructions are available at http://mamitsukalab.org/tools/nmrpro/ Contact: mohamed@kuicr.kyoto-u.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.
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    Adaptive Replication Management in HDFS Based on Supervised Learning (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-05-03
    Description: The number of applications based on Apache Hadoop is dramatically increasing due to the robustness and dynamic features of this system. At the heart of Apache Hadoop, the Hadoop Distributed File System (HDFS) provides the reliability and high availability for computation by applying a static replication by default. However, because of the characteristics of parallel operations on the application layer, the access rate for each data file in HDFS is completely different. Consequently, maintaining the same replication mechanism for every data file leads to detrimental effects on the performance. By rigorously considering the drawbacks of the HDFS replication, this paper proposes an approach to dynamically replicate the data file based on the predictive analysis. With the help of probability theory, the utilization of each data file can be predicted to create a corresponding replication strategy. Eventually, the popular files can be subsequently replicated according to their own access potentials. For the remaining low potential files, an erasure code is applied to maintain the reliability. Hence, our approach simultaneously improves the availability while keeping the reliability in comparison to the default scheme. Furthermore, the complexity reduction is applied to enhance the effectiveness of the prediction when dealing with Big Data.
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    Analytic Queries over Geospatial Time-Series Data Using Distributed Hash Tables (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-05-03
    Description: As remote sensing equipment and networked observational devices continue to proliferate, their corresponding data volumes have surpassed the storage and processing capabilities of commodity computing hardware. This trend has led to the development of distributed storage frameworks that incrementally scale out by assimilating resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of research: extracting insights, relationships, and models from the underlying datasets. The focus of this study is twofold: exploratory and predictive analytics over voluminous, multidimensional datasets in a distributed environment. Both of these types of analysis represent a higher-level abstraction over standard query semantics; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. The algorithms presented in this work were evaluated empirically on a real-world geospatial time-series dataset in a production environment, and are broadly applicable across other storage frameworks.
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    Declarative Framework for Specification, Simulation and Analysis of Distributed Applications (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-05-03
    Description: Researchers have recently shown that declarative database query languages, such as Datalog, could naturally be used to specify and implement network protocols and services. In this paper, we present a declarative framework for the specification, execution, simulation, and analysis of distributed applications. Distributed applications, including routing protocols, can be specified using a Declarative Networking language, called D2C, whose semantics capture the notion of a Distributed State Machine (DSM), i.e., a network of computational nodes that communicate with each other through the exchange of data. The D2C specification can be directly executed using the DSM computational infrastructure of our framework. The same specification can be simulated and formally verified. The simulation component integrates the DSM tool within a network simulation environment and allows developers to simulate network dynamics and collect data about the execution in order to evaluate application responses to network changes. The formal analysis component of our framework, instead, complements the empirical testing by supporting the verification of different classes of properties of distributed algorithms, including convergence of network routing protocols. To demonstrate the generality of our framework, we show how it can be used to analyze two classes of network routing protocols, a path vector and a Mobile Ad-Hoc Network (MANET) routing protocol, and execute a distributed algorithm for pattern formation in multi-robot systems.
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    Structured Dimensionality Reduction for Additive Model Regression (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-05-03
    Description: Additive models are regression methods which model the response variable as the sum of univariate transfer functions of the input variables. Key benefits of additive models are their accuracy and interpretability on many real-world tasks. Additive models are however not adapted to problems involving a large number (e.g., hundreds) of input variables, as they are prone to overfitting in addition to losing interpretability. In this paper, we introduce a novel framework for applying additive models to a large number of input variables. The key idea is to reduce the task dimensionality by deriving a small number of new covariates obtained by linear combinations of the inputs, where the linear weights are estimated with regard to the regression problem at hand. The weights are moreover constrained to prevent overfitting and facilitate the interpretation of the derived covariates. We establish identifiability of the proposed model under mild assumptions and present an efficient approximate learning algorithm. Experiments on synthetic and real-world data demonstrate that our approach compares favorably to baseline methods in terms of accuracy, while resulting in models of lower complexity and yielding practical insights into high-dimensional real-world regression tasks. Our framework broadens the applicability of additive models to high-dimensional problems while maintaining their interpretability and potential to provide practical insights.
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    D3M: detection of differential distributions of methylation levels (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: DNA methylation is an important epigenetic modification related to a variety of diseases including cancers. We focus on the methylation data from Illumina’s Infinium HumanMethylation450 BeadChip. One of the key issues of methylation analysis is to detect the differential methylation sites between case and control groups. Previous approaches describe data with simple summary statistics or kernel function, and then use statistical tests to determine the difference. However, a summary statistics-based approach cannot capture complicated underlying structure, and a kernel function-based approach lacks interpretability of results. Results: We propose a novel method D 3 M, for detection of differential distribution of methylation, based on distribution-valued data. Our method can detect the differences in high-order moments, such as shapes of underlying distributions in methylation profiles, based on the Wasserstein metric. We test the significance of the difference between case and control groups and provide an interpretable summary of the results. The simulation results show that the proposed method achieves promising accuracy and shows favorable results compared with previous methods. Glioblastoma multiforme and lower grade glioma data from The Cancer Genome Atlas show that our method supports recent biological advances and suggests new insights. Availability and Implementation: R implemented code is freely available from https://github.com/ymatts/D3M/ . Contact: ymatsui@med.nagoya-u.ac.jp or shimamura@med.nagoya-u.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online.
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    UniProt-DAAC: domain architecture alignment and classification, a new method for automatic functional annotation in UniProtKB (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-30
    Description: Motivation: Similarity-based methods have been widely used in order to infer the properties of genes and gene products containing little or no experimental annotation. New approaches that overcome the limitations of methods that rely solely upon sequence similarity are attracting increased attention. One of these novel approaches is to use the organization of the structural domains in proteins. Results: We propose a method for the automatic annotation of protein sequences in the UniProt Knowledgebase (UniProtKB) by comparing their domain architectures, classifying proteins based on the similarities and propagating functional annotation. The performance of this method was measured through a cross-validation analysis using the Gene Ontology (GO) annotation of a sub-set of UniProtKB/Swiss-Prot. The results demonstrate the effectiveness of this approach in detecting functional similarity with an average F-score: 0.85. We applied the method on nearly 55.3 million uncharacterized proteins in UniProtKB/TrEMBL resulted in 44 818 178 GO term predictions for 12 172 114 proteins. 22% of these predictions were for 2 812 016 previously non-annotated protein entries indicating the significance of the value added by this approach. Availability and implementation: The results of the method are available at: ftp://ftp.ebi.ac.uk/pub/contrib/martin/DAAC/ . Contact: tdogan@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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    DUDes: a top-down taxonomic profiler for metagenomics (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-30
    Description: Motivation: Species identification and quantification are common tasks in metagenomics and pathogen detection studies. The most recent techniques are built on mapping the sequenced reads against a reference database (e.g. whole genomes, marker genes, proteins) followed by application-dependent analysis steps. Although these methods have been proven to be useful in many scenarios, there is still room for improvement in species and strain level detection, mainly for low abundant organisms. Results: We propose a new method: DUDes, a reference-based taxonomic profiler that introduces a novel top-down approach to analyze metagenomic Next-generation sequencing (NGS) samples. Rather than predicting an organism presence in the sample based only on relative abundances, DUDes first identifies possible candidates by comparing the strength of the read mapping in each node of the taxonomic tree in an iterative manner. Instead of using the lowest common ancestor we propose a new approach: the deepest uncommon descendent. We showed in experiments that DUDes works for single and multiple organisms and can identify low abundant taxonomic groups with high precision. Availability and Implementation: DUDes is open source and it is available at http://sf.net/p/dudes Supplementary information: Supplementary data are available at Bioinformatics online. Contact: renardB@rki.de
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    Genome-scale prediction of moonlighting proteins using diverse protein association information (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-30
    Description: Motivation: Moonlighting proteins (MPs) show multiple cellular functions within a single polypeptide chain. To understand the overall landscape of their functional diversity, it is important to establish a computational method that can identify MPs on a genome scale. Previously, we have systematically characterized MPs using functional and omics-scale information. In this work, we develop a computational prediction model for automatic identification of MPs using a diverse range of protein association information. Results: We incorporated a diverse range of protein association information to extract characteristic features of MPs, which range from gene ontology (GO), protein–protein interactions, gene expression, phylogenetic profiles, genetic interactions and network-based graph properties to protein structural properties, i.e. intrinsically disordered regions in the protein chain. Then, we used machine learning classifiers using the broad feature space for predicting MPs. Because many known MPs lack some proteomic features, we developed an imputation technique to fill such missing features. Results on the control dataset show that MPs can be predicted with over 98% accuracy when GO terms are available. Furthermore, using only the omics-based features the method can still identify MPs with over 75% accuracy. Last, we applied the method on three genomes: Saccharomyces cerevisiae , Caenorhabditis elegans and Homo sapiens , and found that about 2–10% of proteins in the genomes are potential MPs. Availability and Implementation: Code available at http://kiharalab.org/MPprediction Contact: dkihara@purdue.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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    PinaColada: peptide-inhibitor ant colony ad-hoc design algorithm (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-30
    Description: Motivation: Design of protein–protein interaction (PPI) inhibitors is a major challenge in Structural Bioinformatics. Peptides, especially short ones (5–15 amino acid long), are natural candidates for inhibition of protein–protein complexes due to several attractive features such as high structural compatibility with the protein binding site (mimicking the surface of one of the proteins), small size and the ability to form strong hotspot binding connections with the protein surface. Efficient rational peptide design is still a major challenge in computer aided drug design, due to the huge space of possible sequences, which is exponential in the length of the peptide, and the high flexibility of peptide conformations. Results: In this article we present PinaColada, a novel computational method for the design of peptide inhibitors for protein–protein interactions. We employ a version of the ant colony optimization heuristic, which is used to explore the exponential space ( 20n ) of length n peptide sequences, in combination with our fast robotics motivated PepCrawler algorithm, which explores the conformational space for each candidate sequence. PinaColada is being run in parallel, on a DELL PowerEdge 2.8 GHZ computer with 20 cores and 256 GB memory, and takes up to 24 h to design a peptide of 5-15 amino acids length. Availability and implementation: An online server available at: http://bioinfo3d.cs.tau.ac.il/PinaColada/. Contact: danielza@post.tau.ac.il ; wolfson@tau.ac.il
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    Rapid genotype refinement for whole-genome sequencing data using multi-variate normal distributions (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: Whole-genome low-coverage sequencing has been combined with linkage-disequilibrium (LD)-based genotype refinement to accurately and cost-effectively infer genotypes in large cohorts of individuals. Most genotype refinement methods are based on hidden Markov models, which are accurate but computationally expensive. We introduce an algorithm that models LD using a simple multivariate Gaussian distribution. The key feature of our algorithm is its speed. Results: Our method is hundreds of times faster than other methods on the same data set and its scaling behaviour is linear in the number of samples. We demonstrate the performance of the method on both low- and high-coverage samples. Availability and implementation: The source code is available at https://github.com/illumina/marvin Contact: rarthur@illumina.com Supplementary information: Supplementary data are available at Bioinformatics online .
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    HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-30
    Description: Motivation : T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction. Results : We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles. Conclusions : HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods. Availability and implementation : The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy . Contact : nchandra@biochem.iisc.ernet.in Supplementary information: Supplementary data are available at Bioinformatics online.
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    Analysis of CFSE time-series data using division-, age- and label-structured population models (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-30
    Description: Motivation: In vitro and in vivo cell proliferation is often studied using the dye carboxyfluorescein succinimidyl ester (CFSE). The CFSE time-series data provide information about the proliferation history of populations of cells. While the experimental procedures are well established and widely used, the analysis of CFSE time-series data is still challenging. Many available analysis tools do not account for cell age and employ optimization methods that are inefficient (or even unreliable). Results: We present a new model-based analysis method for CFSE time-series data. This method uses a flexible description of proliferating cell populations, namely, a division-, age- and label-structured population model. Efficient maximum likelihood and Bayesian estimation algorithms are introduced to infer the model parameters and their uncertainties. These methods exploit the forward sensitivity equations of the underlying partial differential equation model for efficient and accurate gradient calculation, thereby improving computational efficiency and reliability compared with alternative approaches and accelerating uncertainty analysis. The performance of the method is assessed by studying a dataset for immune cell proliferation. This revealed the importance of different factors on the proliferation rates of individual cells. Among others, the predominate effect of cell age on the division rate is found, which was not revealed by available computational methods. Availability and implementation: The MATLAB source code implementing the models and algorithms is available from http://janhasenauer.github.io/ShAPE-DALSP/ . Contact: jan.hasenauer@helmholtz-muenchen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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    cit: hypothesis testing software for mediation analysis in genomic applications (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: The challenges of successfully applying causal inference methods include: (i) satisfying underlying assumptions, (ii) limitations in data/models accommodated by the software and (iii) low power of common multiple testing approaches. Results: The causal inference test (CIT) is based on hypothesis testing rather than estimation, allowing the testable assumptions to be evaluated in the determination of statistical significance. A user-friendly software package provides P -values and optionally permutation-based FDR estimates ( q -values) for potential mediators. It can handle single and multiple binary and continuous instrumental variables, binary or continuous outcome variables and adjustment covariates. Also, the permutation-based FDR option provides a non-parametric implementation. Conclusion: Simulation studies demonstrate the validity of the cit package and show a substantial advantage of permutation-based FDR over other common multiple testing strategies. Availability and implementation: The cit open-source R package is freely available from the CRAN website ( https://cran.r-project.org/web/packages/cit/index.html ) with embedded C ++ code that utilizes the GNU Scientific Library, also freely available ( http://www.gnu.org/software/gsl/ ). Contact: joshua.millstein@usc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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    SNPhood: investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-30
    Description: Motivation: The vast majority of the many thousands of disease-associated single nucleotide polymorphisms (SNPs) lie in the non-coding part of the genome. They are likely to affect regulatory elements, such as enhancers and promoters, rather than the function of a protein. To understand the molecular mechanisms underlying genetic diseases, it is therefore increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin or transcription factor binding. Results: We developed SNPhood , a user-friendly Bioconductor R package to investigate, quantify and visualise the local epigenetic neighbourhood of a set of SNPs in terms of chromatin marks or TF binding sites using data from NGS experiments. Availability and implementation: SNPhood is publicly available and maintained as an R Bioconductor package at http://bioconductor.org/packages/SNPhood/ . Contact: judith.zaugg@embl.de Supplementary information: Supplementary data are available at Bioinformatics online.
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    Canvas: versatile and scalable detection of copy number variants (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: Versatile and efficient variant calling tools are needed to analyze large scale sequencing datasets. In particular, identification of copy number changes remains a challenging task due to their complexity, susceptibility to sequencing biases, variation in coverage data and dependence on genome-wide sample properties, such as tumor polyploidy or polyclonality in cancer samples. Results: We have developed a new tool, Canvas, for identification of copy number changes from diverse sequencing experiments including whole-genome matched tumor-normal and single-sample normal re-sequencing, as well as whole-exome matched and unmatched tumor-normal studies. In addition to variant calling, Canvas infers genome-wide parameters such as cancer ploidy, purity and heterogeneity. It provides fast and easy-to-run workflows that can scale to thousands of samples and can be easily incorporated into variant calling pipelines. Availability and Implementation: Canvas is distributed under an open source license and can be downloaded from https://github.com/Illumina/canvas . Contact: eroller@illumina.com Supplementary information: Supplementary data are available at Bioinformatics online.
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    pileup.js: a JavaScript library for interactive and in-browser visualization of genomic data (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: p ileup.js is a new browser-based genome viewer. It is designed to facilitate the investigation of evidence for genomic variants within larger web applications. It takes advantage of recent developments in the JavaScript ecosystem to provide a modular, reliable and easily embedded library. Availability and implementation: The code and documentation for pileup.js is publicly available at https://github.com/hammerlab/pileup.js under the Apache 2.0 license. Contact : correspondence@hammerlab.org
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    Pathway analysis by randomization incorporating structure--PARIS: an update (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: Motivation: We present an update to the pathway enrichment analysis tool ‘Pathway Analysis by Randomization Incorporating Structure (PARIS)’ that determines aggregated association signals generated from genome-wide association study results. Pathway-based analyses highlight biological pathways associated with phenotypes. PARIS uses a unique permutation strategy to evaluate the genomic structure of interrogated pathways, through permutation testing of genomic features, thus eliminating many of the over-testing concerns arising with other pathway analysis approaches. Results: We have updated PARIS to incorporate expanded pathway definitions through the incorporation of new expert knowledge from multiple database sources, through customized user provided pathways, and other improvements in user flexibility and functionality. Availability and implementation: PARIS is freely available to all users at https://ritchielab.psu.edu/software/paris-download . Contact: jnc43@case.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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    NSimScan: DNA comparison tool with increased speed, sensitivity and accuracy (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: : Nucleotide Similarity Scanner (NSimScan) is specialized for searching massive DNA databases for distant similarities. Its targeted applications include phylogenomics, comparative and functional studies of non-coding sequences, contamination detection, etc. NSimScan outperforms industry standard tools in combined sensitivity, accuracy and speed, operating at sensitivity similar to BLAST, accuracy of ssearch and speed of MegaBLAST. Availability and implementation: NSimScan is available at https://github.com/abadona/qsimscan as a part of QSimScan package. It is implemented in C ++, distributed under MIT license and supported on Linux, OS X and Windows (with cygwin). Contact: dkaznadzey@yahoo.com Supplementary information: Supplementary data are available at Bioinformatics online.
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    TreeDom: a graphical web tool for analysing domain architecture evolution (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
    Description: : We present TreeDom, a web tool for graphically analysing the evolutionary history of domains in multi-domain proteins. Individual domains on the same protein chain may have distinct evolutionary histories, which is important to grasp in order to understand protein function. For instance, it may be important to know whether a domain was duplicated recently or long ago, to know the origin of inserted domains, or to know the pattern of domain loss within a protein family. TreeDom uses the Pfam database as the source of domain annotations, and displays these on a sequence tree. An advantage of TreeDom is that the user can limit the analysis to N sequences that are most similar to a query, or provide a list of sequence IDs to include. Using the Pfam alignment of the selected sequences, a tree is built and displayed together with the domain architecture of each sequence. Availablility and implementation: http://TreeDom.sbc.su.se Contact: Erik.Sonnhammer@scilifelab.se
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    CellMiner Companion: an interactive web application to explore CellMiner NCI-60 data (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-30
    Description: : The NCI-60 human tumor cell line panel is an invaluable resource for cancer researchers, providing drug sensitivity, molecular and phenotypic data for a range of cancer types. CellMiner is a web resource that provides tools for the acquisition and analysis of quality-controlled NCI-60 data. CellMiner supports queries of up to 150 drugs or genes, but the output is an Excel file for each drug or gene. This output format makes it difficult for researchers to explore the data from large queries. CellMiner Companion is a web application that facilitates the exploration and visualization of output from CellMiner, further increasing the accessibility of NCI-60 data. Availability and Implementation: The web application is freely accessible at https://pul-bioinformatics.shinyapps.io/CellMinerCompanion . The R source code can be downloaded at https://github.com/pepascuzzi/CellMinerCompanion.git . Contact: ppascuzz@purdue.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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    Simulated single molecule microscopy with SMeagol (2016)
    Oxford University Press
    Details
    Publication Date: 2016-07-30
    Description: : SMeagol is a software tool to simulate highly realistic microscopy data based on spatial systems biology models, in order to facilitate development, validation and optimization of advanced analysis methods for live cell single molecule microscopy data. Availability and implementation: SMeagol runs on Matlab R2014 and later, and uses compiled binaries in C for reaction–diffusion simulations. Documentation, source code and binaries for Mac OS, Windows and Ubuntu Linux can be downloaded from http://smeagol.sourceforge.net . Contact: johan.elf@icm.uu.se Supplementary information : Supplementary data are available at Bioinformatics online.
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    Exploring the structure and function of temporal networks with dynamic graphlets (2016)
    Oxford University Press
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    Publication Date: 2016-07-30
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    BMix: probabilistic modeling of occurring substitutions in PAR-CLIP data (2016)
    Oxford University Press
    Details
    Publication Date: 2016-03-26
    Description: Motivation : Photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP) is an experimental method based on next-generation sequencing for identifying the RNA interaction sites of a given protein. The method deliberately inserts T-to-C substitutions at the RNA-protein interaction sites, which provides a second layer of evidence compared with other CLIP methods. However, the experiment includes several sources of noise which cause both low-frequency errors and spurious high-frequency alterations. Therefore, rigorous statistical analysis is required in order to separate true T-to-C base changes, following cross-linking, from noise. So far, most of the existing PAR-CLIP data analysis methods focus on discarding the low-frequency errors and rely on high-frequency substitutions to report binding sites, not taking into account the possibility of high-frequency false positive substitutions. Results : Here, we introduce BMix , a new probabilistic method which explicitly accounts for the sources of noise in PAR-CLIP data and distinguishes cross-link induced T-to-C substitutions from low and high-frequency erroneous alterations. We demonstrate the superior speed and accuracy of our method compared with existing approaches on both simulated and real, publicly available human datasets. Availability and implementation : The model is freely accessible within the BMix toolbox at www.cbg.bsse.ethz.ch/software/BMix , available for Matlab and R. Supplementary information: Supplementary data is available at Bioinformatics online. Contact : niko.beerenwinkel@bsse.ethz.ch
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    Positive and negative forms of replicability in gene network analysis (2016)
    Oxford University Press
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    Publication Date: 2016-03-26
    Description: Motivation: Gene networks have become a central tool in the analysis of genomic data but are widely regarded as hard to interpret. This has motivated a great deal of comparative evaluation and research into best practices. We explore the possibility that this may lead to overfitting in the field as a whole. Results: We construct a model of ‘research communities’ sampling from real gene network data and machine learning methods to characterize performance trends. Our analysis reveals an important principle limiting the value of replication, namely that targeting it directly causes ‘easy’ or uninformative replication to dominate analyses. We find that when sampling across network data and algorithms with similar variability, the relationship between replicability and accuracy is positive (Spearman’s correlation, r s ~0.33) but where no such constraint is imposed, the relationship becomes negative for a given gene function ( r s ~ –0.13). We predict factors driving replicability in some prior analyses of gene networks and show that they are unconnected with the correctness of the original result, instead reflecting replicable biases. Without these biases, the original results also vanish replicably. We show these effects can occur quite far upstream in network data and that there is a strong tendency within protein–protein interaction data for highly replicable interactions to be associated with poor quality control. Availability and implementation: Algorithms, network data and a guide to the code available at: https://github.com/wimverleyen/AggregateGeneFunctionPrediction . Contact: jgillis@cshl.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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    HYBRIDSPADES: an algorithm for hybrid assembly of short and long reads (2016)
    Oxford University Press
    Details
    Publication Date: 2016-03-26
    Description: Motivation: Recent advances in single molecule real-time (SMRT) and nanopore sequencing technologies have enabled high-quality assemblies from long and inaccurate reads. However, these approaches require high coverage by long reads and remain expensive. On the other hand, the inexpensive short reads technologies produce accurate but fragmented assemblies. Thus, a hybrid approach that assembles long reads (with low coverage) and short reads has a potential to generate high-quality assemblies at reduced cost. Results: We describe hybrid SPA des algorithm for assembling short and long reads and benchmark it on a variety of bacterial assembly projects. Our results demonstrate that hybrid SPA des generates accurate assemblies (even in projects with relatively low coverage by long reads) thus reducing the overall cost of genome sequencing. We further present the first complete assembly of a genome from single cells using SMRT reads. Availability and implementation: hybrid SPA des is implemented in C++ as a part of SPAdes genome assembler and is publicly available at http://bioinf.spbau.ru/en/spades Contact: d.antipov@spbu.ru Supplementary information: supplementary data are available at Bioinformatics online.
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    AccessFold: predicting RNA-RNA interactions with consideration for competing self-structure (2016)
    Oxford University Press
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    Publication Date: 2016-03-26
    Description: Motivation: There are numerous examples of RNA–RNA complexes, including microRNA–mRNA and small RNA–mRNA duplexes for regulation of translation, guide RNA interactions with target RNA for post-transcriptional modification and small nuclear RNA duplexes for splicing. Predicting the base pairs formed between two interacting sequences remains difficult, at least in part because of the competition between unimolecular and bimolecular structure. Results: Two algorithms were developed for improved prediction of bimolecular RNA structure that consider the competition between self-structure and bimolecular structure. These algorithms utilize two novel approaches to evaluate accessibility: free energy density minimization and pseudo-energy minimization. Free energy density minimization minimizes the folding free energy change per nucleotide involved in an intermolecular secondary structure. Pseudo-energy minimization (called AccessFold) minimizes the sum of free energy change and a pseudo-free energy penalty for bimolecular pairing of nucleotides that are unlikely to be accessible for bimolecular structure. The pseudo-free energy, derived from unimolecular pairing probabilities, is applied per nucleotide in bimolecular pairs, and this approach is able to predict binding sites that are split by unimolecular structures. A benchmark set of 17 bimolecular RNA structures was assembled to assess structure prediction. Pseudo-energy minimization provides a statistically significant improvement in sensitivity over the method that was found in a benchmark to be the most accurate previously available method, with an improvement from 36.8% to 57.8% in mean sensitivity for base pair prediction. Availability and implementation: Pseudo-energy minimization is available for download as AccessFold, under an open-source license and as part of the RNAstructure package, at: http://rna.urmc.rochester.edu/RNAstructure.html . Contact: david_mathews@urmc.rochester.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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    Sampling ARG of multiple populations under complex configurations of subdivision and admixture (2016)
    Oxford University Press
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    Publication Date: 2016-03-26
    Description: Motivation: Simulating complex evolution scenarios of multiple populations is an important task for answering many basic questions relating to population genomics. Apart from the population samples, the underlying Ancestral Recombinations Graph (ARG) is an additional important means in hypothesis checking and reconstruction studies. Furthermore, complex simulations require a plethora of interdependent parameters making even the scenario-specification highly non-trivial. Results: We present an algorithm SimRA that simulates generic multiple population evolution model with admixture. It is based on random graphs that improve dramatically in time and space requirements of the classical algorithm of single populations. Using the underlying random graphs model, we also derive closed forms of expected values of the ARG characteristics i.e., height of the graph, number of recombinations, number of mutations and population diversity in terms of its defining parameters. This is crucial in aiding the user to specify meaningful parameters for the complex scenario simulations, not through trial-and-error based on raw compute power but intelligent parameter estimation. To the best of our knowledge this is the first time closed form expressions have been computed for the ARG properties. We show that the expected values closely match the empirical values through simulations. Finally, we demonstrate that SimRA produces the ARG in compact forms without compromising any accuracy. We demonstrate the compactness and accuracy through extensive experiments. Availability and implementation : SimRA ( Sim ulation based on R andom graph A lgorithms) source, executable, user manual and sample input-output sets are available for downloading at: https://github.com/ComputationalGenomics/SimRA Contact : parida@us.ibm.com Supplementary information: Supplementary data are available at Bioinformatics online.
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    Maligner: a fast ordered restriction map aligner (2016)
    Oxford University Press
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    Publication Date: 2016-03-26
    Description: Motivation : The Optical Mapping System discovers structural variants and potentiates sequence assembly of genomes via scaffolding and comparisons that globally validate or correct sequence assemblies. Despite its utility, there are few publicly available tools for aligning optical mapping datasets. Results : Here we present software, named ‘Maligner’, for the alignment of both single molecule restriction maps (Rmaps) and in silico restriction maps of sequence contigs to a reference. Maligner provides two modes of alignment: an efficient, sensitive dynamic programming implementation that scales to large eukaryotic genomes, and a faster indexed based implementation for finding alignments with unmatched sites in the reference but not the query. We compare our software to other publicly available tools on Rmap datasets and show that Maligner finds more correct alignments in comparable runtime. Lastly, we introduce the M-Score statistic for normalizing alignment scores across restriction maps and demonstrate its utility for selecting high quality alignments. Availability and implementation : The Maligner software is written in C ++ and is available at https://github.com/LeeMendelowitz/maligner under the GNU General Public License. Contact : mpop@umiacs.umd.edu
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    PopIns: population-scale detection of novel sequence insertions (2016)
    Oxford University Press
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    Publication Date: 2016-03-26
    Description: Motivation: The detection of genomic structural variation (SV) has advanced tremendously in recent years due to progress in high-throughput sequencing technologies. Novel sequence insertions, insertions without similarity to a human reference genome, have received less attention than other types of SVs due to the computational challenges in their detection from short read sequencing data, which inherently involves de novo assembly. De novo assembly is not only computationally challenging, but also requires high-quality data. Although the reads from a single individual may not always meet this requirement, using reads from multiple individuals can increase power to detect novel insertions. Results: We have developed the program PopIns , which can discover and characterize non-reference insertions of 100 bp or longer on a population scale. In this article, we describe the approach we implemented in PopIns. It takes as input a reads-to-reference alignment, assembles unaligned reads using a standard assembly tool, merges the contigs of different individuals into high-confidence sequences, anchors the merged sequences into the reference genome, and finally genotypes all individuals for the discovered insertions. Our tests on simulated data indicate that the merging step greatly improves the quality and reliability of predicted insertions and that PopIns shows significantly better recall and precision than the recent tool MindTheGap. Preliminary results on a dataset of 305 Icelanders demonstrate the practicality of the new approach. Availability and implementation: The source code of PopIns is available from http://github.com/bkehr/popins . Contact: birte.kehr@decode.is Supplementary information: Supplementary data are available at Bioinformatics online.
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    Synchronized navigation and comparative analyses across Ensembl complete bacterial genomes with INSYGHT (2016)
    Oxford University Press
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    Publication Date: 2016-03-26
    Description: Motivation: High-throughput sequencing technologies provide access to an increasing number of bacterial genomes. Today, many analyses involve the comparison of biological properties among many strains of a given species, or among species of a particular genus. Tools that can help the microbiologist with these tasks become increasingly important. Results: Insyght is a comparative visualization tool whose core features combine a synchronized navigation across genomic data of multiple organisms with a versatile interoperability between complementary views. In this work, we have greatly increased the scope of the Insyght public dataset by including 2688 complete bacterial genomes available in Ensembl thus vastly improving its phylogenetic coverage. We also report the development of a virtual machine that allows users to easily set up and customize their own local Insyght server. Availability and implementation: http://genome.jouy.inra.fr/Insyght Contact: Thomas.Lacroix@jouy.inra.fr
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    Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer (2016)
    Oxford University Press
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    Publication Date: 2016-03-26
    Description: : Breast cancer is one of the most frequent cancers among women. Extensive studies into the molecular heterogeneity of breast cancer have produced a plethora of molecular subtype classification and prognosis prediction algorithms, as well as numerous gene expression signatures. However, reimplementation of these algorithms is a tedious but important task to enable comparison of existing signatures and classification models between each other and with new models. Here, we present the genefu R/Bioconductor package, a multi-tiered compendium of bioinformatics algorithms and gene signatures for molecular subtyping and prognostication in breast cancer. Availability and implementation: The genefu package is available from Bioconductor. http://www.bioconductor.org/packages/devel/bioc/html/genefu.html . Source code is also available on Github https://github.com/bhklab/genefu . Contact: bhaibeka@uhnresearch.ca Supplementary information: Supplementary data are available at Bioinformatics online.
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    Inference of Regular Expressions for Text Extraction from Examples (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-04-01
    Description: A large class of entity extraction tasks from text that is either semistructured or fully unstructured may be addressed by regular expressions, because in many practical cases the relevant entities follow an underlying syntactical pattern and this pattern may be described by a regular expression. In this work, we consider the long-standing problem of synthesizing such expressions automatically, based solely on examples of the desired behavior. We present the design and implementation of a system capable of addressing extraction tasks of realistic complexity. Our system is based on an evolutionary procedure carefully tailored to the specific needs of regular expression generation by examples. The procedure executes a search driven by a multiobjective optimization strategy aimed at simultaneously improving multiple performance indexes of candidate solutions while at the same time ensuring an adequate exploration of the huge solution space. We assess our proposal experimentally in great depth, on a number of challenging datasets. The accuracy of the obtained solutions seems to be adequate for practical usage and improves over earlier proposals significantly. Most importantly, our results are highly competitive even with respect to human operators. A prototype is available as a web application at http://regex.inginf.units.it .
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    Pattern Based Sequence Classification (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-04-01
    Description: Sequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting patterns found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of a pattern in a given class of sequences by combining the cohesion and the support of the pattern. We use the discovered patterns to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on an improved version of the existing method of classification based on association rules, while the second ranks the rules by first measuring their value specific to the new data object. Experimental results show that our rule based classifiers outperform existing comparable classifiers in terms of accuracy and stability. Additionally, we test a number of pattern feature based models that use different kinds of patterns as features to represent each sequence as a feature vector. We then apply a variety of machine learning algorithms for sequence classification, experimentally demonstrating that the patterns we discover represent the sequences well, and prove effective for the classification task.
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    Learning Proximity Relations for Feature Selection (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-04-01
    Description: This work presents a feature selection method based on proximity relations learning. Each single feature is treated as a binary classifier that predicts for any three objects X, A, and B whether X is close to A or B. The performance of the classifier is a direct measure of feature quality. Any linear combination of feature-based binary classifiers naturally corresponds to feature selection. Thus, the feature selection problem is transformed into an ensemble learning problem of combining many weak classifiers into an optimized strong classifier. We provide a theoretical analysis of the generalization error of our proposed method which validates the effectiveness of our proposed method. Various experiments are conducted on synthetic data, four UCI data sets and 12 microarray data sets, and demonstrate the success of our approach applying to feature selection. A weakness of our algorithm is high time complexity.
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    Robust Ensemble Clustering Using Probability Trajectories (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
    Details
    Publication Date: 2016-04-01
    Description: Although many successful ensemble clustering approaches have been developed in recent years, there are still two limitations to most of the existing approaches. First, they mostly overlook the issue of uncertain links, which may mislead the overall consensus process. Second, they generally lack the ability to incorporate global information to refine the local links. To address these two limitations, in this paper, we propose a novel ensemble clustering approach based on sparse graph representation and probability trajectory analysis. In particular, we present the elite neighbor selection strategy to identify the uncertain links by locally adaptive thresholds and build a sparse graph with a small number of probably reliable links. We argue that a small number of probably reliable links can lead to significantly better consensus results than using all graph links regardless of their reliability. The random walk process driven by a new transition probability matrix is utilized to explore the global information in the graph. We derive a novel and dense similarity measure from the sparse graph by analyzing the probability trajectories of the random walkers, based on which two consensus functions are further proposed. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of our approach.
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    Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption (2016)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2016-04-01
    Description: The problem we aim to address is the optimization of cost management for executing multiple continuous queries on data streams, where each query is defined by several filters, each of which monitors certain status of the data stream. Specially, the filter can be shared by different queries and expensive to evaluate. The conventional objective for such a problem is to minimize the overall execution cost to solve all queries, by planning the order of filter evaluation in shared strategy. However, in the streaming scenario, the characteristics of data items may change in process, which can bring some uncertainty to the outcome of individual filter evaluation, and affect the plan of query execution as well as the overall execution cost. In our work, considering the influence of the uncertain variation of data characteristics, we propose a framework to deal with the dynamic adjustment of filter ordering for query execution on data stream, and focus on the issues of cost management. By incrementally monitoring and analyzing the results of filter evaluation, our proposed approach can be effectively adaptive to the varied stream behavior and adjust the optimal ordering of filter evaluation, so as to optimize the execution cost. In order to achieve satisfactory performance and efficiency, we also discuss the trade-off between the adaptivity of our framework and the overhead incurred by filter adaption. The experimental results on synthetic and two real data sets (traffic and multimedia) show that our framework can effectively reduce and balance the overall query execution cost and keep high adaptivity in streaming scenario.
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