ALBERT

Description: We examine a distributed detection problem in a wireless sensor network, where sensor nodes collaborate to detect a Gaussian signal with an unknown change of power, i.e., a scale parameter. Due to power/bandwidth constraints, we consider the case where each sensor quantizes its observation into a binary digit. The binary data are then transmitted through error-prone wireless links to a fusion center, where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. We study the design of a binary quantizer based on an asymptotic analysis of the GLRT. Interestingly, the quantization threshold of the quantizer is independent of the unknown scale parameter. Numerical results are included to illustrate the performance of the proposed quantizer and GLRT in binary symmetric channels (BSCs).

Description: More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this work, a two-stage Kalman algorithm method is used to fuse data in hybrid vehicle stability testing. First, the RT3102 navigation system and Dewetron system are introduced. Second, a modeling of data fusion is proposed based on the Kalman filter. Then, this modeling is simulated and tested on a sample vehicle, using Carsim and Simulink software to test the results. The results showed the merits of this modeling.

Description: This installment of Computer's series highlighting the work published in IEEE Computer Society journals comes from IEEE Transactions on Visualization and Computer Graphics. The Web extra at http://youtu.be/E1PVTitj7h0 is a video demonstration of a novel solution to multivariate data visualization that helps users interactively explore data by combining standard presentations, from detailed views to high-level overviews.

Description: The data rearrangement engine (DRE) performs in-memory data restructuring to accelerate irregular, data-intensive applications. An emulation on a field-programmable gate array shows how the DRE could improve speedup, memory bandwidth, and energy consumption on three representative benchmarks.

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Description: Currently deep learning has made great breakthroughs in visual and speech processing, mainly because it draws lessons from the hierarchical mode that brain deals with images and speech. In the field of NLP, a topic model is one of the important ways for modeling documents. Topic models are built on a generative model that clearly does not match the way humans write. In this paper, we propose Event Model, which is unsupervised and based on the language processing mechanism of neurolinguistics, to model documents. In Event Model, documents are descriptions of concrete or abstract events seen, heard, or sensed by people and words are objects in the events. Event Model has two stages: word learning and dimensionality reduction. Word learning is to learn semantics of words based on deep learning. Dimensionality reduction is the process that representing a document as a low dimensional vector by a linear mode that is completely different from topic models. Event Model achieves state-of-the-art results on document retrieval tasks.

Description: Given a database table with records that can be ranked, an interesting problem is to identify selection conditions for the table, which are qualified by an input record and render its ranking as high as possible among the qualifying tuples. In this paper, we study this standing maximization problem, which finds application in object promotion and characterization. After showing the hardness of the problem, we propose greedy methods, which are experimentally shown to achieve high accuracy compared to exhaustive enumeration, while scaling very well to the problem input size. Our contributions include a linear-time algorithm for determining the optimal selection range for an ordinal attribute and techniques for choosing and prioritizing the most promising selection predicates to apply. Experiments on real datasets confirm the effectiveness and efficiency of our techniques.

Description: Some fairly recent research has focused on providing XACML-based solutions for dynamic privacy policy management. In this regard, a number of works have provided enhancements to the performance of XACML policy enforcement point (PEP) component, but very few have focused on enhancing the accuracy of that component. This paper improves the accuracy of an XACML PEP by filling some gaps in the existing works. In particular, dynamically incorporating user access context into the privacy policy decision, and its enforcement. We provide an XACML-based implementation of a dynamic privacy policy management framework and an evaluation of the applicability of our system in comparison to some of the existing approaches.

Description: This paper first introduces pattern aided regression (PXR) models, a new type of regression models designed to represent accurate and interpretable prediction models. This was motivated by two observations: (1) Regression modeling applications often involve complex diverse predictor-response relationships , which occur when the optimal regression models (of given regression model type) fitting two or more distinct logical groups of data are highly different. (2) State-of-the-art regression methods are often unable to adequately model such relationships. This paper defines PXR models using several patterns and local regression models, which respectively serve as logical and behavioral characterizations of distinct predictor-response relationships. The paper also introduces a contrast pattern aided regression (CPXR) method, to build accurate PXR models. In experiments, the PXR models built by CPXR are very accurate in general, often outperforming state-of-the-art regression methods by big margins. Usually using (a) around seven simple patterns and (b) linear local regression models, those PXR models are easy to interpret; in fact, their complexity is just a bit higher than that of (piecewise) linear regression models and is significantly lower than that of traditional ensemble based regression models. CPXR is especially effective for high-dimensional data. The paper also discusses how to use CPXR methodology for analyzing prediction models and correcting their prediction errors.

Description: We analyze models for predicting the probability of a strikeout for a batter/pitcher matchup in baseball using player descriptors that can be estimated accurately from small samples. We start with the log5 model which has been used extensively for describing matchups in sports. Log5 is a special case of a logit model and we use constrained logistic regression over nearly one million matchup observations to assess the use of the log5 explanatory variables for this application. We also show that a batter/pitcher ground ball rate interaction variable is significant for the prediction of strikeout probability and we provide physical justification for the inclusion of this variable in the model. We quantify the differences among the models and show that batters control the majority of the variance in predicted strikeout rate.

Description: This study proposes a quantitative measurement of split of the second heart sound (S2) based on nonstationary signal decomposition to deal with overlaps and energy modeling of the subcomponents of S2. The second heart sound includes aortic (A2) and pulmonic (P2) closure sounds. However, the split detection is obscured due to A2-P2 overlap and low energy of P2. To identify such split, HVD method is used to decompose the S2 into a number of components while preserving the phase information. Further, A2s and P2s are localized using smoothed pseudo Wigner-Ville distribution followed by reassignment method. Finally, the split iscalculated by taking the differences between the means of time indices of A2s and P2s. Experiments on total 33 clips of S2 signals are performed for evaluation of the method. The mean ± standard deviation of the split is 34.7 ± 4.6 ms. The method measures the splitefficiently, even when A2-P2 overlap is ≤ 20 ms and the normalized peak temporal ratio of P2 to A2 is low (≥ 0.22). This proposed method thus, demonstrates its robustness by defining split detectability (SDT), the split detection aptness through detecting P2s, by measuring upto 96 percent. Such findings reveal the effectiveness of the method as competent against the other baselines, especially for A2-P2 overlaps and low energy P2.

Description: Post-acquisition denoising of magnetic resonance (MR) images is an important step to improve any quantitative measurement of the acquired data. In this paper, assuming a Rician noise model, a new filtering method based on the linear minimum mean square error (LMMSE) estimation is introduced, which employs the self-similarity property of the MR data to restore the noise-less signal. This method takes into account the structural characteristics of images and the Bayesian mean square error (Bmse) of the estimator to address the denoising problem. In general, a twofold data processing approach is developed; first, the noisy MR data is processed using a patch-based L 2 -norm similarity measure to provide the primary set of samples required for the estimation process. Afterwards, the Bmse of the estimator is derived as the optimization function to analyze the pre-selected samples and minimize the error between the estimated and the underlying signal. Compared to the LMMSE method and also its recently proposed SNR-adapted realization (SNLMMSE), the optimized way of choosing the samples together with the automatic adjustment of the filtering parameters lead to a more robust estimation performance with our approach. Experimental results show the competitive performance of the proposed method in comparison with related state-of-the-art methods.

Description: Large-scale ad hoc analytics of genomic data is popular using the R-programming language supported by over 700 software packages provided by Bioconductor. More recently, analytical jobs are benefitting from on-demand computing and storage, their scalability and their low maintenance cost, all of which are offered by the cloud. While biologists and bioinformaticists can take an analytical job and execute it on their personal workstations, it remains challenging to seamlessly execute the job on the cloud infrastructure without extensive knowledge of the cloud dashboard. How analytical jobs can not only with minimum effort be executed on the cloud, but also how both the resources and data required by the job can be managed is explored in this paper. An open-source light-weight framework for executing R-scripts using Bioconductor packages, referred to as ‘RBioCloud’, is designed and developed. RBioCloud offers a set of simple command-line tools for managing the cloud resources, the data and the execution of the job. Three biological test cases validate the feasibility of RBioCloud. The framework is available from http://www.rbiocloud.com .

Description: Of major interest to translational genomics is the intervention in gene regulatory networks (GRNs) to affect cell behavior; in particular, to alter pathological phenotypes. Owing to the complexity of GRNs, accurate network inference is practically challenging and GRN models often contain considerable amounts of uncertainty. Considering the cost and time required for conducting biological experiments, it is desirable to have a systematic method for prioritizing potential experiments so that an experiment can be chosen to optimally reduce network uncertainty. Moreover, from a translational perspective it is crucial that GRN uncertainty be quantified and reduced in a manner that pertains to the operational cost that it induces, such as the cost of network intervention. In this work, we utilize the concept of mean objective cost of uncertainty (MOCU) to propose a novel framework for optimal experimental design. In the proposed framework, potential experiments are prioritized based on the MOCU expected to remain after conducting the experiment. Based on this prioritization, one can select an optimal experiment with the largest potential to reduce the pertinent uncertainty present in the current network model. We demonstrate the effectiveness of the proposed method via extensive simulations based on synthetic and real gene regulatory networks.

Description: A novel approach to Contact Map Overlap (CMO) problem is proposed using the two dimensional clusters present in the contact maps. Each protein is represented as a set of the non-trivial clusters of contacts extracted from its contact map. The approach involves finding matching regions between the two contact maps using approximate 2D-pattern matching algorithm and dynamic programming technique. These matched pairs of small contact maps are submitted in parallel to a fast heuristic CMO algorithm. The approach facilitates parallelization at this level since all the pairs of contact maps can be submitted to the algorithm in parallel. Then, a merge algorithm is used in order to obtain the overall alignment. As a proof of concept, MSVNS, a heuristic CMO algorithm is used for global as well as local alignment. The divide and conquer approach is evaluated for two benchmark data sets that of Skolnick and Ding et al. It is interesting to note that along with achieving saving of time, better overlap is also obtained for certain protein folds.

Description: Canalizing genes possess broad regulatory power over a wide swath of regulatory processes. On the other hand, it has been hypothesized that the phenomenon of intrinsically multivariate prediction (IMP) is associated with canalization. However, applications have relied on user-selectable thresholds on the IMP score to decide on the presence of IMP. A methodology is developed here that avoids arbitrary thresholds, by providing a statistical test for the IMP score. In addition, the proposed procedure allows the incorporation of prior knowledge if available, which can alleviate the problem of loss of power due to small sample sizes. The issue of multiplicity of tests is addressed by family-wise error rate (FWER) and false discovery rate (FDR) controlling approaches. The proposed methodology is demonstrated by experiments using synthetic and real gene-expression data from studies on melanoma and ionizing radiation (IR) responsive genes. The results with the real data identified DUSP1 and p53, two well-known canalizing genes associated with melanoma and IR response, respectively, as the genes with a clear majority of IMP predictor pairs. This validates the potential of the proposed methodology as a tool for discovery of canalizing genes from binary gene-expression data. The procedure is made available through an R package.

Description: Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.

Description: A three-step iterative method with fifth-order convergence as a new modification of Newton’s method was presented. This method is for finding multiple roots of nonlinear equation with unknown multiplicity m whose multiplicity m is the highest multiplicity. Its order of convergence is analyzed and proved. Results for some numerical examples show the efficiency of the new method.

Description: License plate recognition is a computer vision method that identifies vehicles from their license plates. The most crucial step of such a system is accurate localization of the plate. The authors propose a system for automatic recognition that has three phases: image capture, plate localization, and license plate number recognition. They tested their methodology on 40 different car models with different types of license plates.

Description: The Regression Network plugin for Cytoscape ( RegNetC ) implements the RegNet algorithm for the inference of transcriptional association network from gene expression profiles. This algorithm is a model tree-based method to detect the relationship between each gene and the remaining genes simultaneously instead of analyzing individually each pair of genes as correlation-based methods do. Model trees are a very useful technique to estimate the gene expression value by regression models and favours localized similarities over more global similarity, which is one of the major drawbacks of correlation-based methods. Here, we present an integrated software suite, named RegNetC , as a Cytoscape plugin that can operate on its own as well. RegNetC facilitates, according to user-defined parameters, the resulted transcriptional gene association network in .sif format for visualization, analysis and interoperates with other Cytoscape plugins, which can be exported for publication figures. In addition to the network, the RegNetC plugin also provides the quantitative relationships between genes expression values of those genes involved in the inferred network, i.e., those defined by the regression models.

Description: Over the past decade or so, several research groups have addressed the problem of multi-label classification where each example can belong to more than one class at the same time. A common approach, called  Binary Relevance (BR) , addresses this problem by inducing a separate classifier for each class. Research has shown that this framework can be improved if mutual class dependence is exploited: an example that belongs to class $X$ is likely to belong also to class $Y$ ; conversely, belonging to $X$ can make an example less likely to belong to $Z$ . Several works sought to model this information by using the vector of class labels as additional example attributes. To fill the unknown values of these attributes during prediction, existing methods resort to using outputs of other classifiers, and this makes them prone to errors. This is where our paper wants to contribute. We identified two potential ways to prune unnecessary dependencies and to reduce error-propagation in our new classifier-stacking technique, which is named PruDent . Experimental results indicate that the classification performance of PruDent compares favorably with that of other state-of-the-art approaches over a broad range of testbeds. Mor- over, its computational costs grow only linearly in the number of classes.

Description: This work deals with the problem of producing a fast and accurate data classification, learning it from a possibly small set of records that are already classified. The proposed approach is based on the framework of the so-called Logical Analysis of Data (LAD), but enriched with information obtained from statistical considerations on the data. A number of discrete optimization problems are solved in the different steps of the procedure, but their computational demand can be controlled. The accuracy of the proposed approach is compared to that of the standard LAD algorithm, of support vector machines and of label propagation algorithm on publicly available datasets of the UCI repository. Encouraging results are obtained and discussed.

Description: A new graph based constrained semi-supervised learning (G-CSSL) framework is proposed. Pairwise constraints (PC) are used to specify the types (intra- or inter-class) of points with labels. Since the number of labeled data is typically small in SSL setting, the core idea of this framework is to create and enrich the PC sets using the propagated soft labels from both labeled and unlabeled data by special label propagation (SLP), and hence obtaining more supervised information for delivering enhanced performance. We also propose a Two-stage Sparse Coding, termed TSC, for achieving adaptive neighborhood for SLP. The first stage aims at correcting the possible corruptions in data and training an informative dictionary, and the second stage focuses on sparse coding. To deliver enhanced inter-class separation and intra-class compactness, we also present a mixed soft-similarity measure to evaluate the similarity/dissimilarity of constrained pairs using the sparse codes and outputted probabilistic values by SLP. Simulations on the synthetic and real datasets demonstrated the validity of our algorithms for data representation and image recognition, compared with other related state-of-the-art graph based semi-supervised techniques.

Description: In large databases, the amount and the complexity of the data calls for data summarization techniques. Such summaries are used to assist fast approximate query answering or query optimization. Histograms are a prominent class of model-free data summaries and are widely used in database systems. So-called self-tuning histograms look at query-execution results to refine themselves. An assumption with such histograms, which has not been questioned so far, is that they can learn the dataset from scratch, that is—starting with an empty bucket configuration. We show that this is not the case. Self-tuning methods are very sensitive to the initial configuration. Three major problems stem from this. Traditional self-tuning is unable to learn projections of multi-dimensional data, is sensitive to the order of queries, and reaches only local optima with high estimation errors. We show how to improve a self-tuning method significantly by starting with a carefully chosen initial configuration. We propose initialization by dense subspace clusters in projections of the data, which improves both accuracy and robustness of self-tuning. Our experiments on different datasets show that the error rate is typically halved compared to the uninitialized version.

Description: Recently, two ideas have been explored that lead to more accurate algorithms for time-series classification (TSC). First, it has been shown that the simplest way to gain improvement on TSC problems is to transform into an alternative data space where discriminatory features are more easily detected. Second, it was demonstrated that with a single data representation, improved accuracy can be achieved through simple ensemble schemes. We combine these two principles to test the hypothesis that forming a collective of ensembles of classifiers on different data transformations improves the accuracy of time-series classification. The collective contains classifiers constructed in the time, frequency, change, and shapelet transformation domains. For the time domain, we use a set of elastic distance measures. For the other domains, we use a range of standard classifiers. Through extensive experimentation on 72 datasets, including all of the 46 UCR datasets, we demonstrate that the simple collective formed by including all classifiers in one ensemble is significantly more accurate than any of its components and any other previously published TSC algorithm. We investigate alternative hierarchical collective structures and demonstrate the utility of the approach on a new problem involving classifying Caenorhabditis elegans mutant types.

Description: In real-world graphs such as social networks, Semantic Web and biological networks, each vertex usually contains rich information, which can be modeled by a set of tokens or elements. In this paper, we study a subgraph matching with set similarity (SMS $^2$ ) query over a large graph database, which retrieves subgraphs that are structurally isomorphic to the query graph, and meanwhile satisfy the condition of vertex pair matching with the (dynamic) weighted set similarity. To efficiently process the SMS $^2$ query, this paper designs a novel lattice-based index for data graph, and lightweight signatures for both query vertices and data vertices. Based on the index and signatures, we propose an efficient two-phase pruning strategy including set similarity pruning and structure-based pruning, which exploits the unique features of both (dynamic) weighted set similarity and graph topology. We also propose an efficient dominating-set-based subgraph matching algorithm guided by a dominating set selection algorithm to achieve better query performance. Extensive experiments on both real and synthetic datasets demonstrate that our method outperforms state-of-the-art methods by an order of magnitude.

Description: Data imputation aims at filling in missing attribute values in databases. Most existing imputation methods to string attribute values are inferring-based approaches, which usually fail to reach a high imputation recall by just inferring missing values from the complete part of the data set. Recently, some retrieving-based methods are proposed to retrieve missing values from external resources such as the World Wide Web, which tend to reach a much higher imputation recall, but inevitably bring a large overhead by issuing a large number of search queries. In this paper, we investigate the interaction between the inferring-based methods and the retrieving-based methods. We show that retrieving a small number of selected missing values can greatly improve the imputation recall of the inferring-based methods. With this intuition, we propose an inTeractive Retrieving-Inferring data imPutation approach (TRIP), which performs retrieving and inferring alternately in filling in missing attribute values in a data set. To ensure the high recall at the minimum cost, TRIP faces a challenge of selecting the least number of missing values for retrieving to maximize the number of inferable values. Our proposed solution is able to identify an optimal retrieving-inferring scheduling scheme in deterministic data imputation, and the optimality of the generated scheme is theoretically analyzed with proofs. We also analyze with an example that the optimal scheme is not feasible to be achieved in $tau$ -constrained stochastic data imputation ( $tau$ -SDI), but still, our proposed solution identifies an expected-optimal scheme in $tau$ -SDI. Extensive experiments on four data collections show that TRIP retrieves on average 20 percent missing values and achieves the same high recall that was reached by the retrieving-based approach.

Description: Visual classification has attracted considerable research interests in the past decades. In this paper, a novel $ell _1$ -hypergraph model for visual classification is proposed. Hypergraph learning, as a natural extension of graph model, has been widely used in many machine learning tasks. In previous work, hypergraph is usually constructed by attribute-based or neighborhood-based methods. That is, a hyperedge is generated by connecting a set of samples sharing a same feature attribute or in a neighborhood. However, these methods are unable to explore feature space globally or sensitive to noises. To address these problems, we propose a novel hypergraph construction approach that leverages sparse representation to generate hyperedges and learns the relationship among hyperedges and their vertices. First, for each sample, a hyperedge is generated by regarding it as the centroid and linking it as well as its nearest neighbors. Then, the sparse representation method is applied to represent the centroid vertex by other vertices within the same hyperedge. The vertices with zero coefficients are removed from the hyperedge. Finally, the representation coefficients are used to define the incidence relation between the hyperedge and the vertices. In our approach, we also optimize the hyperedge weights to modulate the effects of different hyperedges. We leverage the prior knowledge on the hyperedges so that the hyperedges sharing more vertices can have closer weights, where a graph Laplacian is used to regularize the optimization of the weights. Our approach is named $ell _1$ -hypergraph since the $ell _1$ sparse representation is employed in the hypergraph construction process. The method is evaluated on various visual classification tasks, and it demonstrates promising performance.

Description: We introduce a new method for normalization of data acquired by liquid chromatography coupled with mass spectrometry (LC-MS) in label-free differential expression analysis. Normalization of LC-MS data is desired prior to subsequent statistical analysis to adjust variabilities in ion intensities that are not caused by biological differences but experimental bias. There are different sources of bias including variabilities during sample collection and sample storage, poor experimental design, noise, etc. In addition, instrument variability in experiments involving a large number of LC-MS runs leads to a significant drift in intensity measurements. Although various methods have been proposed for normalization of LC-MS data, there is no universally applicable approach. In this paper, we propose a Bayesian normalization model (BNM) that utilizes scan-level information from LC-MS data. Specifically, the proposed method uses peak shapes to model the scan-level data acquired from extracted ion chromatograms (EIC) with parameters considered as a linear mixed effects model. We extended the model into BNM with drift (BNMD) to compensate for the variability in intensity measurements due to long LC-MS runs. We evaluated the performance of our method using synthetic and experimental data. In comparison with several existing methods, the proposed BNM and BNMD yielded significant improvement.

Description: Performing clustering analysis is one of the important research topics in cancer discovery using gene expression profiles, which is crucial in facilitating the successful diagnosis and treatment of cancer. While there are quite a number of research works which perform tumor clustering, few of them considers how to incorporate fuzzy theory together with an optimization process into a consensus clustering framework to improve the performance of clustering analysis. In this paper, we first propose a random double clustering based cluster ensemble framework (RDCCE) to perform tumor clustering based on gene expression data. Specifically, RDCCE generates a set of representative features using a randomly selected clustering algorithm in the ensemble, and then assigns samples to their corresponding clusters based on the grouping results. In addition, we also introduce the random double clustering based fuzzy cluster ensemble framework (RDCFCE), which is designed to improve the performance of RDCCE by integrating the newly proposed fuzzy extension model into the ensemble framework. RDCFCE adopts the normalized cut algorithm as the consensus function to summarize the fuzzy matrices generated by the fuzzy extension models, partition the consensus matrix, and obtain the final result. Finally, adaptive RDCFCE (A-RDCFCE) is proposed to optimize RDCFCE and improve the performance of RDCFCE further by adopting a self-evolutionary process (SEPP) for the parameter set. Experiments on real cancer gene expression profiles indicate that RDCFCE and A-RDCFCE works well on these data sets, and outperform most of the state-of-the-art tumor clustering algorithms.

Description: Named-entity recognition (NER) plays an important role in the development of biomedical databases. However, the existing NER tools produce multifarious named-entities which may result in both curatable and non-curatable markers. To facilitate biocuration with a straightforward approach, classifying curatable named-entities is helpful with regard to accelerating the biocuration workflow. Co-occurrence Interaction Nexus with Named-entity Recognition (CoINNER) is a web-based tool that allows users to identify genes, chemicals, diseases, and action term mentions in the Comparative Toxicogenomic Database (CTD). To further discover interactions, CoINNER uses multiple advanced algorithms to recognize the mentions in the BioCreative IV CTD Track. CoINNER is developed based on a prototype system that annotated gene, chemical, and disease mentions in PubMed abstracts at BioCreative 2012 Track I (literature triage). We extended our previous system in developing CoINNER. The pre-tagging results of CoINNER were developed based on the state-of-the-art named entity recognition tools in BioCreative III. Next, a method based on conditional random fields (CRFs) is proposed to predict chemical and disease mentions in the articles. Finally, action term mentions were collected by latent Dirichlet allocation (LDA). At the BioCreative IV CTD Track, the best F-measures reached for gene/protein, chemical/drug and disease NER were 54 percent while CoINNER achieved a 61.5 percent F-measure. System URL: http://ikmbio.csie.ncku.edu.tw/coinner/introduction.htm.

Description: Next-generation short-read sequencing is widely utilized in genomic studies. Biological applications require an alignment step to map sequencing reads to the reference genome, before acquiring expected genomic information. This requirement makes alignment accuracy a key factor for effective biological interpretation. Normally, when accounting for measurement errors and single nucleotide polymorphisms, short read mappings with a few mismatches are generally considered acceptable. However, to further improve the efficiency of short-read sequencing alignment, we propose a method to retrieve additional reliably aligned reads (reads with more than a pre-defined number of mismatches), using a Bayesian-based approach. In this method, we first retrieve the sequence context around the mismatched nucleotides within the already aligned reads; these loci contain the genomic features where sequencing errors occur. Then, using the derived pattern, we evaluate the remaining (typically discarded) reads with more than the allowed number of mismatches, and calculate a score that represents the probability that a specific alignment is correct. This strategy allows the extraction of more reliably aligned reads, therefore improving alignment sensitivity. Implementation: The source code of our tool, ResSeq, can be downloaded from: https://github.com/hrbeubiocenter/Resseq.

Description: In genome assembly graphs, motifs such as tips, bubbles, and cross links are studied in order to find sequencing errors and to understand the nature of the genome. Superbubble, a complex generalization of bubbles, was recently proposed as an important subgraph class for analyzing assembly graphs. At present, a quadratic time algorithm is known. This paper gives an -time algorithm to solve this problem for a graph with $m$ edges.

Description: The papers in this special section focus on software and databases that are central in bioinformatics and computational biology.. These programs are playing more and more important roles in biology and medical research. These papers cover a broad range of topics, including computational genomics and transcriptomics, analysis of biological networks and interactions, drug design, biomedical signal/image analysis, biomedical text mining and ontologies, biological data mining, visualization and integration, and high performance computing application in bioinformatics.

Description: In the computational biology community, machine learning algorithms are key instruments for many applications, including the prediction of gene-functions based upon the available biomolecular annotations. Additionally, they may also be employed to compute similarity between genes or proteins. Here, we describe and discuss a software suite we developed to implement and make publicly available some of such prediction methods and a computational technique based upon Latent Semantic Indexing (LSI), which leverages both inferred and available annotations to search for semantically similar genes. The suite consists of three components. BioAnnotationPredictor is a computational software module to predict new gene-functions based upon Singular Value Decomposition of available annotations. SimilBio is a Web module that leverages annotations available or predicted by BioAnnotationPredictor to discover similarities between genes via LSI. The suite includes also SemSim , a new Web service built upon these modules to allow accessing them programmatically. We integrated SemSim in the Bio Search Computing framework (http://www.bioinformatics.deib.polimi.it/bio-seco/seco/), where users can exploit the Search Computing technology to run multi-topic complex queries on multiple integrated Web services. Accordingly, researchers may obtain ranked answers involving the computation of the functional similarity between genes in support of biomedical knowledge discovery.

Description: Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personali- ed cancer therapy.

Description: The papers in this special issue contain extended versions of works that were originally presented at the Brazilian Symposium on Bioinformatics 2013 (BSB 2013), held in Recife, Brazil, November 3-6, 2013.

Description: Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions. This paper presents a supervised learning framework based on Bayesian inference for combining two types of information: i) network topology information, and ii) information related to proteins and the interactions between them. The motivation of our model is that by combining these two types of information one can achieve a better accuracy in predicting protein-protein interactions, than by using models constructed from these two types of information independently.

Description: We develop a theory of algebraic operations over linear and context-free grammars that makes it possible to combine simple “atomic” grammars operating on single sequences into complex, multi-dimensional grammars. We demonstrate the utility of this framework by constructing the search spaces of complex alignment problems on multiple input sequences explicitly as algebraic expressions of very simple one-dimensional grammars. In particular, we provide a fully worked frameshift-aware, semiglobal DNA-protein alignment algorithm whose grammar is composed of products of small, atomic grammars. The compiler accompanying our theory makes it easy to experiment with the combination of multiple grammars and different operations. Composite grammars can be written out in $ {rm L}^AT_{E}X$ for documentation and as a guide to implementation of dynamic programming algorithms. An embedding in Haskell as a domain-specific language makes the theory directly accessible to writing and using grammar products without the detour of an external compiler. Software and supplemental files available here: http://www.bioinf.uni-leipzig.de/Software/gramprod/

Description: Recent advancements in genomics and proteomics provide a solid foundation for understanding the pathogenesis of diabetes. Proteomics of diabetes associated pathways help to identify the most potent target for the management of diabetes. The relevant datasets are scattered in various prominent sources which takes much time to select the therapeutic target for the clinical management of diabetes. However, additional information about target proteins is needed for validation. This lacuna may be resolved by linking diabetes associated genes, pathways and proteins and it will provide a strong base for the treatment and planning management strategies of diabetes. Thus, a web source “Diabetes Associated Proteins Database (DAPD)” has been developed to link the diabetes associated genes, pathways and proteins using PHP, MySQL. The current version of DAPD has been built with proteins associated with different types of diabetes. In addition, DAPD has been linked to external sources to gain the access to more participatory proteins and their pathway network. DAPD will reduce the time and it is expected to pave the way for the discovery of novel anti-diabetic leads using computational drug designing for diabetes management. DAPD is open accessed via following url www.mkarthikeyan.bioinfoau.org/dapd.

Description: Determining the glycan topology automatically from mass spectra represents a great challenge. Existing methods fall into approximate and exact ones. The former including greedy and heuristic ones can reduce the computational complexity, but suffer from information lost in the procedure of glycan interpretation. The latter including dynamic programming and exhaustive enumeration are much slower than the former. In the past years, nearly all emerging methods adopted a tree structure to represent a glycan. They share such problems as repetitive peak counting in reconstructing a candidate structure. Besides, tree-based glycan representation methods often have to give different computational formulas for binary and ternary glycans. We propose a new directed acyclic graph structure for glycan representation. Based on it, this work develops a de novo algorithm to accurately reconstruct the tree structure iteratively from mass spectra with logical constraints and some known biosynthesis rules, by a single computational formula. The experiments on multiple complex glycans extracted from human serum show that the proposed algorithm can achieve higher accuracy to determine a glycan topology than prior methods without increasing computational burden.

Description: A crucial step in understanding the architecture of cells and tissues from microscopy images, and consequently explain important biological events such as wound healing and cancer metastases, is the complete extraction and enumeration of individual filaments from the cellular cytoskeletal network. Current efforts at quantitative estimation of filament length distribution, architecture and orientation from microscopy images are predominantly limited to visual estimation and indirect experimental inference. Here we demonstrate the application of a new algorithm to reliably estimate centerlines of biological filament bundles and extract individual filaments from the centerlines by systematically disambiguating filament intersections. We utilize a filament enhancement step followed by reverse diffusion based filament localization and an integer programming based set combination to systematically extract accurate filaments automatically from microscopy images. Experiments on simulated and real confocal microscope images of flat cells (2D images) show efficacy of the new method.

Description: The Local/Global Alignment (Zemla, 2003), or LGA, is a popular method for the comparison of protein structures. One of the two components of LGA requires us to compute the longest common contiguous segments between two protein structures. That is, given two structures $A=(a_1, ldots , a_n)$ and $B=(b_1, ldots , b_n)$ where $a_k$ , $b_kin mathbb {R}^3$ , we are to find, among all the segments $f=(a_i,ldots ,a_j)$ and $g=(b_i,ldots ,b_j)$ that fulfill a certain criterion regarding their similarity, those of the maximum length. We consider the following criteria: (1) the root mean squared deviation (RMSD) between $f$ and $g$ is to be within a given $tin mathbb {R}$ ; (2) $f$ and $g$ can be superposed such that for each $k$ , $ile kle j$ , $Vert a_k-b_kVert le t$ for a given $tin mathbb {R}$ . We give an algorithm of $O(n;log; n+n{{boldsymbol l}})$ time complexity when the first requirement applies, where ${{boldsymbol l}}$ is the maximum length of the segments fulfilling the criterion. We show an FPTAS which, for any

Description: Noise can induce various dynamical behaviors in nonlinear systems. White noise perturbed systems have been extensively investigated during the last decades. In gene networks, experimentally observed extrinsic noise is colored. As an attempt, we investigate the genetic toggle switch systems perturbed by colored extrinsic noise and with kinetic parameters. Compared with white noise perturbed systems, we show there also exists optimal colored noise strength to induce the best stochastic switch behaviors in the single toggle switch, and the best synchronized switching in the networked systems, which demonstrate that noise-induced optimal switch behaviors are widely in existence. Moreover, under a wide range of system parameter regions, we find there exist wider ranges of white and colored noises strengths to induce good switch and synchronization behaviors, respectively; therefore, white noise is beneficial for switch and colored noise is beneficial for population synchronization. Our observations are very robust to extrinsic stimulus strength, cell density, and diffusion rate. Finally, based on the Waddington’s epigenetic landscape and the Wiener-Khintchine theorem, physical mechanisms underlying the observations are interpreted. Our investigations can provide guidelines for experimental design, and have potential clinical implications in gene therapy and synthetic biology.

Description: Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network remains a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work, we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution algorithm (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms for PPI networks more accurately. We tested our method for its power in differentiating models and estimating parameters on simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show duplication attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks.

Description: Single nucleotide polymorphisms, a dominant type of genetic variants, have been used successfully to identify defective genes causing human single gene diseases. However, most common human diseases are complex diseases and caused by gene-gene and gene-environment interactions. Many SNP-SNP interaction analysis methods have been introduced but they are not powerful enough to discover interactions more than three SNPs. The paper proposes a novel method that analyzes all SNPs simultaneously. Different from existing methods, the method regards an individual’s genotype data on a list of SNPs as a point with a unit of energy in a multi-dimensional space, and tries to find a new coordinate system where the energy distribution difference between cases and controls reaches the maximum. The method will find different multiple SNPs combinatorial patterns between cases and controls based on the new coordinate system. The experiment on simulated data shows that the method is efficient. The tests on the real data of age-related macular degeneration (AMD) disease show that it can find out more significant multi-SNP combinatorial patterns than existing methods.

Description: Disulfide connectivity is an important protein structural characteristic. Accurately predicting disulfide connectivity solely from protein sequence helps to improve the intrinsic understanding of protein structure and function, especially in the post-genome era where large volume of sequenced proteins without being functional annotated is quickly accumulated. In this study, a new feature extracted from the predicted protein 3D structural information is proposed and integrated with traditional features to form discriminative features. Based on the extracted features, a random forest regression model is performed to predict protein disulfide connectivity. We compare the proposed method with popular existing predictors by performing both cross-validation and independent validation tests on benchmark datasets. The experimental results demonstrate the superiority of the proposed method over existing predictors. We believe the superiority of the proposed method benefits from both the good discriminative capability of the newly developed features and the powerful modelling capability of the random forest. The web server implementation, called TargetDisulfide, and the benchmark datasets are freely available at: http://csbio.njust.edu.cn/bioinf/TargetDisulfide for academic use.

Description: Proteins are molecules that form the mass of living beings. These proteins exist in dissociated forms like amino-acids and carry out various biological functions, in fact, almost all body reactions occur with the participation of proteins. This is one of the reasons why the analysis of proteins has become a major issue in biology. In a more concrete way, the identification of conserved patterns in a set of related protein sequences can provide relevant biological information about these protein functions. In this paper, we present a novel algorithm based on teaching learning based optimization (TLBO) combined with a local search function specialized to predict common patterns in sets of protein sequences. This population-based evolutionary algorithm defines a group of individuals (solutions) that enhance their knowledge (quality) by means of different learning stages. Thus, if we correctly adapt it to the biological context of the mentioned problem, we can get an acceptable set of quality solutions. To evaluate the performance of the proposed technique, we have used six instances composed of different related protein sequences obtained from the PROSITE database. As we will see, the designed approach makes good predictions and improves the quality of the solutions found by other well-known biological tools.

Description: We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

Description: In this paper, we present three improvements to a three-point third order variant of Newton’s method derived from the Simpson rule. The first one is a fifth order method using the same number of functional evaluations as the third order method, the second one is a four-point 10th order method and the last one is a five-point 20th order method. In terms of computational point of view, our methods require four evaluations (one function and three first derivatives) to get fifth order, five evaluations (two functions and three derivatives) to get 10th order and six evaluations (three functions and three derivatives) to get 20th order. Hence, these methods have efficiency indexes of 1.495, 1.585 and 1.648, respectively which are better than the efficiency index of 1.316 of the third order method. We test the methods through some numerical experiments which show that the 20th order method is very efficient.

Description: Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Due to the complex background, current algorithms have some unsolved issues with false alarm rate. In order to reduce the false alarm rate, an infrared small target detection algorithm based on saliency detection and support vector machine was proposed. Firstly, we detect salient regions that may contain targets with phase spectrum Fourier transform (PFT) approach. Then, target recognition was performed in the salient regions. Experimental results show the proposed algorithm has ideal robustness and efficiency for real infrared small target detection applications.

Description: In dynamic propagation environments, beamforming algorithms may suffer from strong interference, steering vector mismatches, a low convergence speed and a high computational complexity. Reduced-rank signal processing techniques provide a way to address the problems mentioned above. This paper presents a low-complexity robust data-dependent dimensionality reduction based on an iterative optimization with steering vector perturbation (IOVP) algorithm for reduced-rank beamforming and steering vector estimation. The proposed robust optimization procedure jointly adjusts the parameters of a rank reduction matrix and an adaptive beamformer. The optimized rank reduction matrix projects the received signal vector onto a subspace with lower dimension. The beamformer/steering vector optimization is then performed in a reduced dimension subspace. We devise efficient stochastic gradient and recursive least-squares algorithms for implementing the proposed robust IOVP design. The proposed robust IOVP beamforming algorithms result in a faster convergence speed and an improved performance. Simulation results show that the proposed IOVP algorithms outperform some existing full-rank and reduced-rank algorithms with a comparable complexity.

Description: Recently, wireless sensor networks (WSNs) have drawn great interest due to their outstanding monitoring and management potential in medical, environmental and industrial applications. Most of the applications that employ WSNs demand all of the sensor nodes to run on a common time scale, a requirement that highlights the importance of clock synchronization. The clock synchronization problem in WSNs is inherently related to parameter estimation. The accuracy of clock synchronization algorithms depends essentially on the statistical properties of the parameter estimation algorithms. Recently, studies dedicated to the estimation of synchronization parameters, such as clock offset and skew, have begun to emerge in the literature. The aim of this article is to provide an overview of the state-of-the-art clock synchronization algorithms for WSNs from a statistical signal processing point of view. This article focuses on describing the key features of the class of clock synchronization algorithms that exploit the traditional two-way message (signal) exchange mechanism. Upon introducing the two-way message exchange mechanism, the main clock offset estimation algorithms for pairwise synchronization of sensor nodes are first reviewed, and their performance is compared. The class of fully-distributed clock offset estimation algorithms for network-wide synchronization is then surveyed. The paper concludes with a list of open research problems pertaining to clock synchronization of WSNs.

Description: Genes can participate in multiple biological processes at a time and thus their expression can be seen as a composition of the contributions from the active processes. Biclustering under a plaid assumption allows the modeling of interactions between transcriptional modules or biclusters (subsets of genes with coherence across subsets of conditions) by assuming an additive composition of contributions in their overlapping areas. Despite the biological interest of plaid models, few biclustering algorithms consider plaid effects and, when they do, they place restrictions on the allowed types and structures of biclusters, and suffer from robustness problems by seizing exact additive matchings. We propose BiP (Biclustering using Plaid models), a biclustering algorithm with relaxations to allow expression levels to change in overlapping areas according to biologically meaningful assumptions (weighted and noise-tolerant composition of contributions). BiP can be used over existing biclustering solutions (seizing their benefits) as it is able to recover excluded areas due to unaccounted plaid effects and detect noisy areas non-explained by a plaid assumption, thus producing an explanatory model of overlapping transcriptional activity. Experiments on synthetic data support BiP’s efficiency and effectiveness. The learned models from expression data unravel meaningful and non-trivial functional interactions between biological processes associated with putative regulatory modules.

Description: We propose a classifier system called iPFPi that predicts the functions of un-annotated proteins. iPFPi assigns an un-annotated protein $P$ the functions of GO annotation terms that are semantically similar to $P$ . An un-annotated protein $P$ and a GO annotation term $T$ are represented by their characteristics. The characteristics of $P$ are GO terms found within the abstracts of biomedical literature associated with $P$ . The characteristics of $T$ are GO terms found within the abstracts of biomedical literature associated with the proteins annotated with the function of $T$ . Let - F$ and $Fprime $ be the important (dominant) sets of characteristic terms representing $T$ and $P$ , respectively. iPFPi would annotate $P$ with the function of $T$ , if $F$ and $Fprime $ are semantically similar. We constructed a novel semantic similarity measure that takes into consideration several factors, such as the dominance degree of each characteristic term $t$ in set $F$

Description: Adverse drug reaction (ADR) is a common clinical problem, sometimes accompanying with high risk of mortality and morbidity. It is also one of the major factors that lead to failure in new drug development. Unfortunately, most of current experimental and computational methods are unable to evaluate clinical safety of drug candidates in early drug discovery stage due to the very limited knowledge of molecular mechanisms underlying ADRs. Therefore, in this study, we proposed a novel naïve Bayesian model for rapid assessment of clinical ADRs with frequency estimation. This model was constructed on a gene-ADR association network, which covered 611 US FDA approved drugs, 14,251 genes, and 1,254 distinct ADR terms. An average detection rate of 99.86 and 99.73 percent were achieved eventually in identification of known ADRs in internal test data set and external case analyses respectively. Moreover, a comparative analysis between the estimated frequencies of ADRs and their observed frequencies was undertaken. It is observed that these two frequencies have the similar distribution trend. These results suggest that the naïve Bayesian model based on gene-ADR association network can serve as an efficient and economic tool in rapid ADRs assessment.

Description: We consider the problem of adaptively routing a fleet of cooperative vehicles within a road network in the presence of uncertain and dynamic congestion conditions. To tackle this problem, we first propose a Gaussian process dynamic congestion model that can effectively characterize both the dynamics and the uncertainty of congestion conditions. Our model is efficient and thus facilitates real-time adaptive routing in the face of uncertainty. Using this congestion model, we develop efficient algorithms for non-myopic adaptive routing to minimize the collective travel time of all vehicles in the system. A key property of our approach is the ability to efficiently reason about the long-term value of exploration, which enables collectively balancing the exploration/exploitation trade-off for entire fleets of vehicles. Our approach is validated by traffic data from two large Asian cities. Our congestion model is shown to be effective in modeling dynamic congestion conditions. Our routing algorithms also generate significantly faster routes compared to standard baselines, and achieve near-optimal performance compared to an omniscient routing algorithm. We also present the results from a preliminary field study, which showcases the efficacy of our approach.

Description: Betweenness centrality is a classic measure that quantifies the importance of a graph element (vertex or edge) according to the fraction of shortest paths passing through it. This measure is notoriously expensive to compute, and the best known algorithm runs in $mathcal {O}(nm)$ time. The problems of efficiency and scalability are exacerbated in a dynamic setting, where the input is an evolving graph seen edge by edge, and the goal is to keep the betweenness centrality up to date. In this paper, we propose the first truly scalable algorithm for online computation of betweenness centrality of both vertices and edges in an evolving graph where new edges are added and existing edges are removed. Our algorithm is carefully engineered with out-of-core techniques and tailored for modern parallel stream processing engines that run on clusters of shared-nothing commodity hardware. Hence, it is amenable to real-world deployment. We experiment on graphs that are two orders of magnitude larger than previous studies. Our method is able to keep the betweenness centrality measures up-to-date online, i.e., the time to update the measures is smaller than the inter-arrival time between two consecutive updates.

Description: Phase change memory (PCM) is non-volatile memory that is byte-addressable. It is two to four times denser than DRAM, orders of magnitude better than NAND Flash memory in read latency, and 10 times better than NAND Flash memory in write endurance. However, it still limits the number of write operations to at most $10^6$ times per PCM cell. To extend its lifetime, it is necessary to evenly distribute write operations over all the memory cells. Up to now, the $mathrm{B^{+}}$ -Tree index structure has been used to quickly locate a search key in a relational database management system (RDBMS). All the record keys in each node are sorted and packed upon insertion in, and deletion from, the $mathrm{B^{+}}$ -Tree. In addition, a counter keeps track of the number of valid keys in the $mathrm{B^{+}}$ -Tree. Consequently, a $mathrm{B^{+}}$ -Tree algorithm results in a large number of write operations, which deteriorates the endurance of PCM. This restricts the usage of PCM on a database server and deteriorates performance of database servers. In this paper, we propose a novel PCM-aware $math- m{B^{+}}$ -Tree index structure, called $mathrm{PB^{+}}$ -Tree, to provide wear-leveling in PCM. According to our experiment results, $mathrm{PB^{+}}$ -Tree is much faster than the existing $mathrm{B^{+}}$ -Tree algorithms for PCM and NAND Flash memory with versatile workloads. More importantly, our scheme also greatly reduces the number of write operations compared to other $mathrm{B^{+}}$ -Tree algorithms. All of these results suggest that $mathrm{PB^{+}}$ -Tree is the $mathrm{B^{+}}$ -Tree algorithm best fitted to PCM.

Description: The papers in this special section were presented at the 13th International Workshop on Data Mining in Bioinformatics (BIOKDD???14) was organized in conjunction with the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining that was held on August 24, 2014 in New York, NY. It brought together international researchers in the interacting disciplines of data mining, systems biology, and bioinformatics at the Bloomberg Headquarters venue. The goal of this workshop is to encourage Knowledge Discovery and Data mining (KDD) researchers to take on the numerous challenges that Bioinformatics offers.

Description: The introduction of next-generation sequencing technologies has radically changed the way we view structural genetic events. Microhomology-mediated break-induced replication (MMBIR) is just one of the many mechanisms that can cause genomic destabilization that may lead to cancer. Although the mechanism for MMBIR remains unclear, it has been shown that MMBIR is typically associated with template-switching events. Currently, to our knowledge, there is no existing bioinformatics tool to detect these template-switching events. We have developed MMBIRFinder, a method that detects template-switching events associated with MMBIR from whole-genome sequenced data. MMBIRFinder uses a half-read alignment approach to identify potential regions of interest. Clustering of these potential regions helps narrow the search space to regions with strong evidence. Subsequent local alignments identify the template-switching events with single-nucleotide accuracy. Using simulated data, MMBIRFinder identified 83 percent of the MMBIR regions within a five nucleotide tolerance. Using real data, MMBIRFinder identified 16 MMBIR regions on a normal breast tissue data sample and 51 MMBIR regions on a triple-negative breast cancer tumor sample resulting in detection of 37 novel template-switching events. Finally, we identified template-switching events residing in the promoter region of seven genes that have been implicated in breast cancer. The program is freely available for download at https://github.com/msegar/MMBIRFinder.

Description: Compressing heterogeneous collections of trees is an open problem in computational phylogenetics. In a heterogeneous tree collection, each tree can contain a unique set of taxa. An ideal compression method would allow for the efficient archival of large tree collections and enable scientists to identify common evolutionary relationships over disparate analyses. In this paper, we extend TreeZip to compress heterogeneous collections of trees. TreeZip is the most efficient algorithm for compressing homogeneous tree collections. To the best of our knowledge, no other domain-based compression algorithm exists for large heterogeneous tree collections or enable their rapid analysis. Our experimental results indicate that TreeZip averages 89.03 percent (72.69 percent) space savings on unweighted (weighted) collections of trees when the level of heterogeneity in a collection is moderate. The organization of the TRZ file allows for efficient computations over heterogeneous data. For example, consensus trees can be computed in mere seconds. Lastly, combining the TreeZip compressed (TRZ) file with general-purpose compression yields average space savings of 97.34 percent (81.43 percent) on unweighted (weighted) collections of trees. Our results lead us to believe that TreeZip will prove invaluable in the efficient archival of tree collections, and enables scientists to develop novel methods for relating heterogeneous collections of trees.

Description: The papers in this special section were presented at the 2014 International Conference on Genome Informatics (GIW).

Description: Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.

Description: Structural domains are evolutionary and functional units of proteins and play a critical role in comparative and functional genomics. Computational assignment of domain function with high reliability is essential for understanding whole-protein functions. However, functional annotations are conventionally assigned onto full-length proteins rather than associating specific functions to the individual structural domains. In this article, we present Structural Domain Annotation (SDA), a novel computational approach to predict functions for SCOP structural domains. The SDA method integrates heterogeneous information sources, including structure alignment based protein-SCOP mapping features, InterPro2GO mapping information, PSSM Profiles, and sequence neighborhood features, with a Bayesian network. By large-scale annotating Gene Ontology terms to SCOP domains with SDA, we obtained a database of SCOP domain to Gene Ontology mappings, which contains $sim$ 162,000 out of the approximately 166,900 domains in SCOPe 2.03 ( $>$ 97 percent) and their predicted Gene Ontology functions. We have benchmarked SDA using a single-domain protein dataset and an independent dataset from different species. Comparative studies show that SDA significantly outperforms the existing function prediction methods for structural domains in terms of coverage and maximum F-measure.

Description: A major challenge in computational biology is to find simple representations of high-dimensional data that best reveal the underlying structure. In this work, we present an intuitive and easy-to-implement method based on ranked neighborhood comparisons that detects structure in unsupervised data. The method is based on ordering objects in terms of similarity and on the mutual overlap of nearest neighbors. This basic framework was originally introduced in the field of social network analysis to detect actor communities. We demonstrate that the same ideas can successfully be applied to biomedical data sets in order to reveal complex underlying structure. The algorithm is very efficient and works on distance data directly without requiring a vectorial embedding of data. Comprehensive experiments demonstrate the validity of this approach. Comparisons with state-of-the-art clustering methods show that the presented method outperforms hierarchical methods as well as density based clustering methods and model-based clustering. A further advantage of the method is that it simultaneously provides a visualization of the data. Especially in biomedical applications, the visualization of data can be used as a first pre-processing step when analyzing real world data sets to get an intuition of the underlying data structure. We apply this model to synthetic data as well as to various biomedical data sets which demonstrate the high quality and usefulness of the inferred structure.

Description: Proline residues are common source of kinetic complications during folding. The X-Pro peptide bond is the only peptide bond for which the stability of the cis and trans conformations is comparable. The cis-trans isomerization (CTI) of X-Pro peptide bonds is a widely recognized rate-limiting factor, which can not only induces additional slow phases in protein folding but also modifies the millisecond and sub-millisecond dynamics of the protein. An accurate computational prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. In our earlier work, we successfully developed a biophysically motivated proline CTI predictor utilizing a novel tree-based consensus model with a powerful metalearning technique and achieved 86.58 percent Q2 accuracy and 0.74 Mcc, which is a better result than the results (70-73 percent Q2 accuracies) reported in the literature on the well-referenced benchmark dataset. In this paper, we describe experiments with novel randomized subspace learning and bootstrap seeding techniques as an extension to our earlier work, the consensus models as well as entropy-based learning methods, to obtain better accuracy through a precise and robust learning scheme for proline CTI prediction.

Description: Efficient search algorithms for finding genomic-range overlaps are essential for various bioinformatics applications. A majority of fast algorithms for searching the overlaps between a query range (e.g., a genomic variant) and a set of N reference ranges (e.g., exons) has time complexity of O ( k + log N ), where k denotes a term related to the length and location of the reference ranges. Here, we present a simple but efficient algorithm that reduces k, based on the maximum reference range length. Specifically, for a given query range and the maximum reference range length, the proposed method divides the reference range set into three subsets: always , potentially , and never overlapping . Therefore, search effort can be reduced by excluding never overlapping subset. We demonstrate that the running time of the proposed algorithm is proportional to potentially overlapping subset size, that is proportional to the maximum reference range length if all the other conditions are the same. Moreover, an implementation of our algorithm was 13.8 to 30.0 percent faster than one of the fastest range search methods available when tested on various genomic-range data sets. The proposed algorithm has been incorporated into a disease-linked variant prioritization pipeline for WGS (http://gnome.tchlab.org) and its implementation is available at http://ml.ssu.ac.kr/gSearch.

Description: The identification of protein complexes in protein-protein interaction (PPI) networks is fundamental for understanding biological processes and cellular molecular mechanisms. Many graph computational algorithms have been proposed to identify protein complexes from PPI networks by detecting densely connected groups of proteins. These algorithms assess the density of subgraphs through evaluation of the sum of individual edges or nodes; thus, incomplete and inaccurate measures may miss meaningful biological protein complexes with functional significance. In this study, we propose a novel method for assessing the compactness of local subnetworks by measuring the number of three node cliques. The present method detects each optimal cluster by growing a seed and maximizing the compactness function. To demonstrate the efficacy of the new proposed method, we evaluate its performance using five PPI networks on three reference sets of yeast protein complexes with five different measurements and compare the performance of the proposed method with four state-of-the-art methods. The results show that the protein complexes generated by the proposed method are of better quality than those generated by four classic methods. Therefore, the new proposed method is effective and useful for detecting protein complexes in PPI networks.

Description: A proposed property-aware name service simultaneously supports what, where, and when properties of each IoT object through unique, text-based, and human-readable identity assignments.

Description: Since its inception, the Internet has been a complex landscape for developers as well as users to negotiate. The new ISO/IEC/IEEE 23026 standard seeks to improve websites' usability and information content and simplify Web service management and maintenance activities.

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Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: IEEE president-elect candidates address Computer Society concerns.

Description: Events of interest to Computer Society members.

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Description: Although you can't see something that isn't there, you can perceive the powerful effects of its nonexistence. The Web extra at http://youtu.be/vahyX-lM9AI is an audio recording of author David Alan Grier expanding on his Errant Hashtag column, in which he talks about the lack of connection that can exist between two people or organizations.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Irregular applications present unpredictable memory-access patterns, data-dependent control flow, and fine-grained data transfers. Only a holistic view spanning all layers of the hardware and software stack can provide effective solutions to address these challenges.

Description: Computer scientist Anil Jain discusses the evolution of the biometric recognition field. The first Web extra at http://youtu.be/Wb_JbpdFoz4 is a video in which Anil Jain talks with Charles Severance about the evolution of the biometric recognition field. The second Web extra at http://youtu.be/_NdKzKybyQ4 is an audio recording in which author Charles Severance reads his Computing Conversations column that discusses his interview with Anil Jain about the evolution of the biometric recognition field.

Description: A summary of articles recently published in IEEE Computer Society periodicals.

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Description: Emerging data-intensive applications attempt to process and provide insight into vast amounts of online data. A new class of linear algebra algorithms can efficiently execute sparse matrix-matrix and matrix-vector multiplications on large-scale, shared memory multiprocessor systems, enabling analysts to more easily discern meaningful data relationships, such as those in social networks.

Description: Work on human self-awareness is the basis for a framework to develop computational systems that can adaptively manage complex dynamic tradeoffs at runtime. An architectural case study in cloud computing illustrates the framework's potential benefits.

Description: Executing irregular, data-intensive workloads on multithreaded architectures can result in performance losses and scalability problems. Codesigning algorithms and architectures can realize high performance on irregular applications. A codesign study reveals four key lessons learned from implementing matching algorithms on various platforms

Description: The acronym TSA could just as well stand for "tactics to suppress accountability." It's an object lesson in the misuse of technology toward ill-defined ends.

Description: A summary of articles published in Computer 32 and 16 years ago.

Description: Students at the University of Brasília set out to build a better exercise bike. The Web extra at http://youtu.be/NCSb_sDJL7c is a video demonstration of the Bike-X simulator, a virtual cycling experience designed by engineering students at the University of Brasília's Gama Campus.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: The Galois system can automatically parallelize irregular algorithms written in a serial programming model and execute them efficiently on nonuniform memory access (NUMA) machines. Experimental results for five complex irregular algorithms show that the system scales up to 420× on large NUMA systems at 512 threads.

Description: The authors discuss important factors to consider when migrating software to the cloud and offer recommendations to maximize the chance of success.

Description: The $k$ nearest neighbor ( $k$ NN) search on road networks is an important function in web mapping services. These services are now dealing with rapidly arriving queries, that are issued by a massive amount of users. While overlay graph-based indices can answer shortest path queries efficiently, there have been no studies on utilizing such indices to answer $k$ NN queries efficiently. In this paper, we fill this research gap and present two efficient $k$ NN search solutions on overlay graph-based indices. Experimental results show that our solutions offer very low query latency (0.1 ms) and require only small index sizes, even for 10-million-node networks.

Description: Measuring semantic similarity between two terms is essential for a variety of text analytics and understanding applications. Currently, there are two main approaches for this task, namely the knowledge based and the corpus based approaches. However, existing approaches are more suitable for semantic similarity between words rather than the more general multi-word expressions (MWEs), and they do not scale very well. Contrary to these existing techniques, we propose an efficient and effective approach for semantic similarity using a large scale semantic network. This semantic network is automatically acquired from billions of web documents. It consists of millions of concepts, which explicitly model the context of semantic relationships. In this paper, we first show how to map two terms into the concept space, and compare their similarity there. Then, we introduce a clustering approach to orthogonalize the concept space in order to improve the accuracy of the similarity measure. Finally, we conduct extensive studies to demonstrate that our approach can accurately compute the semantic similarity between terms of MWEs and with ambiguity, and significantly outperforms 12 competing methods under Pearson Correlation Coefficient. Meanwhile, our approach is much more efficient than all competing algorithms, and can be used to compute semantic similarity in a large scale.

Description: Given a spatio-temporal network, a source, a destination, and a desired departure time interval, the All-departure-time Lagrangian Shortest Paths (ALSP) problem determines a set which includes the shortest path for every departure time in the given interval. ALSP is important for critical societal applications such as eco-routing. However, ALSP is computationally challenging due to the non-stationary ranking of the candidate paths across distinct departure-times. Current related work for reducing the redundant work, across consecutive departure-times sharing a common solution, exploits only partial information e.g., the earliest feasible arrival time of a path. In contrast, our approach uses all available information, e.g., the entire time series of arrival times for all departure-times. This allows elimination of all knowable redundant computation based on complete information available at hand. We operationalize this idea through the concept of critical-time-points (CTP), i.e., departure-times before which ranking among candidate paths cannot change. In our preliminary work, we proposed a CTP based forward search strategy. In this paper, we propose a CTP based temporal bi-directional search for the ALSP problem via a novel impromptu rendezvous termination condition. Theoretical and experimental analysis show that the proposed approach outperforms the related work approaches particularly when there are few critical-time-points.

Description: Computing connected components is a core operation on graph data. Since billion-scale graphs cannot be resident in memory of a single server, several approaches based on distributed machines have recently been proposed. The representative methods are $mathsf{Hashhbox{-}Tohbox{-}Min}$ and $mathsf{PowerGraph}$ . $mathsf{Hashhbox{-}Tohbox{-}Min}$ is the state-of-the art disk-based distributed method which minimizes the number of MapReduce rounds. $mathsf{PowerGraph}$ is the-state-of-the-art in-memory distributed system, which is typically faster than the disk-based distributed one, however, requires a lot of machines for handling billion-scale graphs. In this paper, we propose an I/O efficient parallel algorithm for billion-scale graphs in a single PC. We first propose the Disk-based Sequential access-oriented Parallel processing  (DSP) model that exploits sequential disk access in terms of disk I/Os and parallel processing in terms of computation. We then propose an ultra-fast disk-based parallel algorithm for computing connected components, $mathsf{DSPhbox{-}CC}$ , which largely improves the performance through sequential disk scan and page-level cache-conscious parallel processing . Extensive experimental results show that $mathsf{DSPhbox{-}CC}$ 1) computes connected components in billion-scale graphs using the limited memory size whereas in-memory algorithms can only support medium-sized graphs with the same memory size, and 2) significantly outperforms all distributed competitors as well as a representative disk-based parallel method.

Description: Answering why-not questions in databases is promised to have wide application prospect in many areas and thereby, has attracted recent attention in the database research community. This paper addresses the problem of answering these so-called why-not questions in similar graph matching for graph databases. Given a set of answer graphs of an initial query graph $q$ and a set of missing ( why-not ) graphs, we aim to modify $q$ into a new query graph $q^*$ such that the missing graphs are included in the new answer set of $q^*$ . We present an approximate solution to address the above as the optimal solution is NP-hard to compute. In our approach, we first compute the bounded search space and the distance to be minimized for $q^*$ . Then, we present a two-phase algorithm to find the new query $q^*$ . In the first phase, we generate a set of candidate edges to be added/deleted into/from the initial query $q$ within the bounded search space and in the second phase, we select a subset of candidate edges generated in the first phase to minimize the distance for $q^*$ . We also demonstrate the effectiveness and efficiency of our approach by conducting extensive experiments on two real datasets.

Description: How has the interdisciplinary data mining field been practiced in Network and Systems Management (NSM)? In Science and Technology, there is a wide use of data mining in areas like bioinformatics, genetics, Web, and, more recently, astroinformatics. However, the application in NSM has been limited and inconsiderable. In this article, we provide an account of how data mining has been applied in managing networks and systems for the past four decades, presumably since its birth. We look into the field’s applications in the key NSM activities—discovery, monitoring, analysis, reporting, and domain knowledge acquisition. In the end, we discuss our perspective on the issues that are considered critical for the effective application of data mining in the modern systems which are characterized by heterogeneity and high dynamism.