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  • Articles  (346)
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  • 2017  (346)
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  • IEEE/ACM Transactions on Computational Biology and Bioinformatics  (176)
  • IEEE Geoscience and Remote Sensing Magazine  (91)
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  • Computer Science  (346)
  • 1
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    Table of Contents (2017)
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    Publication Date: 2017-03-21
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    The Fifth Year of Our Magazine Is in Print [From the Editor] (2017)
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  • 3
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    Staff Listing (2017)
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    Publication Date: 2017-03-21
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  • 4
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    Advanced Spectral Classifiers for Hyperspectral Images: A review (2017)
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    Publication Date: 2017-03-21
    Description: Hyperspectral image classification has been a vibrant area of research in recent years. Given a set of observations, i.e., pixel vectors in a hyperspectral image, classification approaches try to allocate a unique label to each pixel vector. However, the classification of hyperspectral images is a challenging task for a number of reasons, such as the presence of redundant features, the imbalance among the limited number of available training samples, and the high dimensionality of the data.
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  • 5
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    Front Cover (2017)
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  • 6
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    GRSS Has Big Plans for 2017 [President's Message] (2017)
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  • 7
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    Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques (2017)
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    Publication Date: 2017-03-21
    Description: Land-cover mapping in remote sensing (RS) applications renders rich information for decision support and environmental monitoring systems. The derivation of such information increasingly relies on robust classification methods for identifying the complex land-cover area of different categories. Numerous classification techniques have been designed for the analysis of RS imagery. In this context, support vector machines (SVMs) have recently received increasing interest. However, the need for a small-size training set remains a bottleneck to design efficient supervised classifiers, while an adequate number of unlabeled data is readily available in RS images and can be exploited as a supplementary source of information. To fully leverage these precious unlabeled data, a number of promising advanced SVM-based methods, such as active SVMs, semisupervised SVMs (S3VMs), and SVMs combined with other algorithms, have been developed to analyze satellite imagery. In this literature review, we have surveyed these learning techniques to explore RS images. Moreover, we have provided the empirical evidences of SVMs and three representative techniques. It is our hope that this review will provide guidelines to future researchers to enhance further algorithmic developments in RS applications.
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  • 8
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    CALL FOR PAPERS IEEE Geoscience and Remote Sensing Magazine (2017)
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  • 9
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    The IEEE United Arab Emirates Section Chapter Is Gearing Up to Support the National Space Program [Chapters] (2017)
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  • 10
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    2016 IEEE GRSS Data Fusion Contest: Multitemporal Very High Resolution from Space [Technical Committees] (2017)
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    Publication Date: 2017-03-21
    Description: Since 2006, the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) organizes a yearly Data Fusion Contest that aims to promote the use of new remote sensing data sources and stimulating new methodological developments [1]-[10].
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  • 11
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    Distinguished Lecturers Can Talk at Your Chapter Meeting [Distinguished Lecturer Program] (2017)
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  • 12
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    Alejandro Frery's Distinguished Lecturer Visit to Shanghai [Distinguished Lecturer Program] (2017)
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    Publication Date: 2017-03-21
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  • 13
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    A Generalized Lattice Based Probabilistic Approach for Metagenomic Clustering (2017)
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    Publication Date: 2017-08-14
    Description: Metagenomics involves the analysis of genomes of microorganisms sampled directly from their environment. Next Generation Sequencing allows a high-throughput sampling of small segments from genomes in the metagenome to generate reads. To study the properties and relationships of the microorganisms present, clustering can be performed based on the inherent composition of the sampled reads for unknown species. We propose a two-dimensional lattice based probabilistic model for clustering metagenomic datasets. The occurrence of a species in the metagenome is estimated using a lattice of probabilistic distributions over small sized genomic sequences. The two dimensions denote distributions for different sizes and groups of words, respectively. The lattice structure allows for additional support for a node from its neighbors when the probabilistic support for the species using the parameters of the current node is deemed insufficient. We also show convergence for our algorithm. We test our algorithm on simulated metagenomic data containing bacterial species and observe more than $85\text{percent}$ precision. We also evaluate our algorithm on an in vitro -simulated bacterial metagenome and on human patient data, and show a better clustering than other algorithms even for short reads and varied abundance. The software and datasets can be downloaded from https:// github.com/lattclus/lattice-metage .
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    Topics: Biology , Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computational Intelligence Society ; The IEEE Computer Society ; The IEEE Control Systems Society ; The IEEE Engineering in Medicine and Biology Society ; The Association for Computing Machinery.
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  • 14
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    A New Feature Vector Based on Gene Ontology Terms for Protein-Protein Interaction Prediction (2017)
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    Publication Date: 2017-08-14
    Description: Protein-protein interaction (PPI) plays a key role in understanding cellular mechanisms in different organisms. Many supervised classifiers like Random Forest (RF) and Support Vector Machine (SVM) have been used for intra or inter-species interaction prediction. For improving the prediction performance, in this paper we propose a novel set of features to represent a protein pair using their annotated Gene Ontology (GO) terms, including their ancestors. In our approach, a protein pair is treated as a document (bag of words), where the terms annotating the two proteins represent the words. Feature value of each word is calculated using information content of the corresponding term multiplied by a coefficient, which represents the weight of that term inside a document (i.e., a protein pair). We have tested the performance of the classifier using the proposed feature on different well known data sets of different species like S. cerevisiae, H. Sapiens, E. Coli, and D. melanogaster . We compare it with the other GO based feature representation technique, and demonstrate its competitive performance.
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  • 15
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    A Novel Adaptive Penalized Logistic Regression for Uncovering Biomarker Associated with Anti-Cancer Drug Sensitivity (2017)
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    Publication Date: 2017-08-14
    Description: We propose a novel adaptive penalized logistic regression modeling strategy based on Wilcoxon rank sum test (WRST) to effectively uncover driver genes in classification. In order to incorporate significance of gene in classification, we first measure significance of each gene by gene ranking method based on WRST, and then the adaptive L $_{1}$ -type penalty is discriminately imposed on each gene depending on the measured importance degree of gene. The incorporating significance of genes into adaptive logistic regression enables us to impose a large amount of penalty on low ranking genes, and thus noise genes are easily deleted from the model and we can effectively identify driver genes. Monte Carlo experiments and real world example are conducted to investigate effectiveness of the proposed approach. In Sanger data analysis, we introduce a strategy to identify expression modules indicating gene regulatory mechanisms via the principal component analysis (PCA), and perform logistic regression modeling based on not a single gene but gene expression modules. We can see through Monte Carlo experiments and real world example that the proposed adaptive penalized logistic regression outperforms feature selection and classification compared with existing L $_{1}$ -type regularization. The discriminately imposed penalty based on WRST effectively performs crucial gene selection, and thus our method can improve classification accuracy without interruption of noise genes. Furthermore, it can be seen through Sanger data analysis that the method for gene expression modules based on principal components and their loading scores provides interpretable results in biological viewpoints.
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  • 16
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    Detection Copy Number Variants from NGS with Sparse and Smooth Constraints (2017)
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    Publication Date: 2017-08-14
    Description: It is known that copy number variations (CNVs) are associated with complex diseases and particular tumor types, thus reliable identification of CNVs is of great potential value. Recent advances in next generation sequencing (NGS) data analysis have helped manifest the richness of CNV information. However, the performances of these methods are not consistent. Reliably finding CNVs in NGS data in an efficient way remains a challenging topic, worthy of further investigation. Accordingly, we tackle the problem by formulating CNVs identification into a quadratic optimization problem involving two constraints. By imposing the constraints of sparsity and smoothness, the reconstructed read depth signal from NGS is anticipated to fit the CNVs patterns more accurately. An efficient numerical solution tailored from alternating direction minimization (ADM) framework is elaborated. We demonstrate the advantages of the proposed method, namely ADM-CNV, by comparing it with six popular CNV detection methods using synthetic, simulated, and empirical sequencing data. It is shown that the proposed approach can successfully reconstruct CNV patterns from raw data, and achieve superior or comparable performance in detection of the CNVs compared to the existing counterparts.
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  • 17
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    Omics Informatics: From Scattered Individual Software Tools to Integrated Workflow Management Systems (2017)
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    Publication Date: 2017-08-14
    Description: Omic data analyses pose great informatics challenges. As an emerging subfield of bioinformatics, omics informatics focuses on analyzing multi-omic data efficiently and effectively, and is gaining momentum. There are two underlying trends in the expansion of omics informatics landscape: the explosion of scattered individual omics informatics tools with each of which focuses on a specific task in both single- and multi- omic settings, and the fast-evolving integrated software platforms such as workflow management systems that can assemble multiple tools into pipelines and streamline integrative analysis for complicated tasks. In this survey, we give a holistic view of omics informatics, from scattered individual informatics tools to integrated workflow management systems. We not only outline the landscape and challenges of omics informatics, but also sample a number of widely used and cutting-edge algorithms in omics data analysis to give readers a fine-grained view. We survey various workflow management systems (WMSs), classify them into three levels of WMSs from simple software toolkits to integrated multi-omic analytical platforms, and point out the emerging needs for developing intelligent workflow management systems. We also discuss the challenges, strategies and some existing work in systematic evaluation of omics informatics tools. We conclude by providing future perspectives of emerging fields and new frontiers in omics informatics.
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  • 18
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    Super-Thresholding: Supervised Thresholding of Protein Crystal Images (2017)
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    Publication Date: 2017-08-14
    Description: In general, a single thresholding technique is developed or enhanced to separate foreground objects from background for a domain of images. This idea may not generate satisfactory results for all images in a dataset, since different images may require different types of thresholding methods for proper binarization or segmentation. To overcome this limitation, in this study, we propose a novel approach called “super-thresholding” that utilizes a supervised classifier to decide an appropriate thresholding method for a specific image. This method provides a generic framework that allows selection of the best thresholding method among different thresholding techniques that are beneficial for the problem domain. A classifier model is built using features extracted priori from the original image only or posteriori by analyzing the outputs of thresholding methods and the original image. This model is applied to identify the thresholding method for new images of the domain. We performed our method on protein crystallization images, and then we compared our results with six thresholding techniques. Numerical results are provided using four different correctness measurements. Super-thresholding outperforms the best single thresholding method around 10 percent, and it gives the best performance for protein crystallization dataset in our experiments.
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  • 19
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    Prediction of Novel Drugs for Hepatocellular Carcinoma Based on Multi-Source Random Walk (2017)
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    Publication Date: 2017-08-14
    Description: Computational approaches for predicting drug-disease associations by integrating gene expression and biological network provide great insights to the complex relationships among drugs, targets, disease genes, and diseases at a system level. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high rate of morbidity and mortality. We provide an integrative framework to p redict novel d rugs for HCC based on m ulti-source r andom w alk (PD-MRW). Firstly, based on gene expression and protein interaction network, we construct a g ene-gene w eighted i nteraction n etwork (GWIN). Then, based on multi-source random walk in GWIN, we build a drug-drug similarity network. Finally, based on the known drugs for HCC, we score all drugs in the drug-drug similarity network. The robustness of our predictions, their overlap with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched KEGG pathway demonstrate our approach can effectively identify new drug indications. Specifically, regorafenib (Rank = 9 in top-20 list) is proven to be effective in Phase I and II clinical trials of HCC, and the Phase III trial is ongoing. And, it has 11 overlapping pathways with HCC with lower p-values. Focusing on a particular disease, we believe our approach is more accurate and possesses better scalability.
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  • 20
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    Unmanned Aerial Vehicles and Spatial Thinking: Boarding Education With Geotechnology And Drones (2017)
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    Publication Date: 2017-09-20
    Description: The recent boom in the number and importance of unmanned aerial vehicles (UAVs), such as drones, unmanned aircraft systems (UASs), and remotely piloted aircraft systems (RPASs), has placed the geosciences and remote sensing (RS) community in a privileged position. But the increasing market demand for a geoenabled workforce contrasts markedly with the number of college-level students enrolling in the related disciplines. This article focuses on current and future opportunities for incorporating UAVs, geosciences, and RS as part of education programs to engage incoming students (and society more broadly) in this set of emerging technologies. Specifically, we will review the current status of geosciences and RS education involving UAVs, including a strengths, weaknesses, opportunities, and threats (SWOT) matrix and a vision toward the future. In short, it is essential that we systematize, disseminate, and universalize topics related to geosciences and RS education in terms of UAVs because the fields are growing exponentially, and the trend is expected to continue.
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  • 21
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    GRSM Call for Papers (2017)
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    Publication Date: 2017-09-20
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  • 22
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    IEEE GRSM Now Included in Thomson Reuters?s Journal Citation Report [From the Editor] (2017)
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  • 23
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    IEEE GRSS Accomplishes New Milestones [President's Message] (2017)
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  • 24
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    A Value-Chain Analysis for the Copernicus Earth Observation Infrastructure Evolution: A Knowledgebase of Users, Needs, Services, and Products (2017)
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    Publication Date: 2017-09-20
    Description: This article reviews and analyzes the needs of Earth observation (EO) services' users, stakeholders, and beneficiaries. It identifies the key elements of the value chain of the European EO infrastructure and builds a comprehensive knowledgebase of those elements, represented as a relational database. The entities in the database are users, needs, services, and products. The database also includes connections between these entities-such as users to needs and products to services-via mapping tables. Leveraging data from the relevant policy and requirement documents as well as from research project reports, the database contains 63 users, 37 explicit needs, and 95 EO products across six Copernicus services.
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  • 25
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    The RapidScat Ocean Winds Scatterometer: A Radar System Engineering Perspective (2017)
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    Publication Date: 2017-09-20
    Description: The NASA International Space Station (ISS)-RapidScat scatterometer operated on board the ISS from October 2014 into August 2016. It was developed using a combination of new subsystems and spare SeaWinds scatterometer engineering model subsystems to interface with the ISS. Using commercial (nonflight-qualified) parts in the new assemblies, developing RapidScat required a relatively small budget and short time schedule (just over two years). This article describes RapidScat's development from the perspective of radar system engineering, particularly in relation to performance requirements and testing.
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    An IR-Based Approach Utilizing Query Expansion for Plagiarism Detection in MEDLINE (2017)
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    Publication Date: 2017-08-14
    Description: The identification of duplicated and plagiarized passages of text has become an increasingly active area of research. In this paper, we investigate methods for plagiarism detection that aim to identify potential sources of plagiarism from MEDLINE, particularly when the original text has been modified through the replacement of words or phrases. A scalable approach based on Information Retrieval is used to perform candidate document selection—the identification of a subset of potential source documents given a suspicious text—from MEDLINE. Query expansion is performed using the ULMS Metathesaurus to deal with situations in which original documents are obfuscated. Various approaches to Word Sense Disambiguation are investigated to deal with cases where there are multiple Concept Unique Identifiers (CUIs) for a given term. Results using the proposed IR-based approach outperform a state-of-the-art baseline based on Kullback-Leibler Distance.
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    Topics: Biology , Computer Science
    Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Computational Intelligence Society ; The IEEE Computer Society ; The IEEE Control Systems Society ; The IEEE Engineering in Medicine and Biology Society ; The Association for Computing Machinery.
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  • 27
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    SuperMIC: Analyzing Large Biological Datasets in Bioinformatics with Maximal Information Coefficient (2017)
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    Publication Date: 2017-08-14
    Description: The maximal information coefficient (MIC) has been proposed to discover relationships and associations between pairs of variables. It poses significant challenges for bioinformatics scientists to accelerate the MIC calculation, especially in genome sequencing and biological annotations. In this paper, we explore a parallel approach which uses MapReduce framework to improve the computing efficiency and throughput of the MIC computation. The acceleration system includes biological data storage on HDFS, preprocessing algorithms, distributed memory cache mechanism, and the partition of MapReduce jobs. Based on the acceleration approach, we extend the traditional two-variable algorithm to multiple variables algorithm. The experimental results show that our parallel solution provides a linear speedup comparing with original algorithm without affecting the correctness and sensitivity.
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  • 28
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    Brain Modulyzer: Interactive Visual Analysis of Functional Brain Connectivity (2017)
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    Publication Date: 2017-08-14
    Description: We present Brain Modulyzer, an interactive visual exploration tool for functional magnetic resonance imaging (fMRI) brain scans, aimed at analyzing the correlation between different brain regions when resting or when performing mental tasks. Brain Modulyzer combines multiple coordinated views—such as heat maps, node link diagrams, and anatomical views—using brushing and linking to provide an anatomical context for brain connectivity data. Integrating methods from graph theory and analysis, e.g. , community detection and derived graph measures, makes it possible to explore the modular and hierarchical organization of functional brain networks. Providing immediate feedback by displaying analysis results instantaneously while changing parameters gives neuroscientists a powerful means to comprehend complex brain structure more effectively and efficiently and supports forming hypotheses that can then be validated via statistical analysis. To demonstrate the utility of our tool, we present two case studies—exploring progressive supranuclear palsy, as well as memory encoding and retrieval.
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  • 29
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    Circular Order Aggregation and Its Application to Cell-Cycle Genes Expressions (2017)
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    Publication Date: 2017-08-14
    Description: The aim of circular order aggregation is to find a circular order on a set of $n$ items using angular values from $p$ heterogeneous data sets. This problem is new in the literature and has been motivated by the biological question of finding the order among the peak expression of a group of cell cycle genes. In this paper, two very different approaches to solve the problem that use pairwise and triplewise information are proposed. Both approaches are analyzed and compared using theoretical developments and numerical studies, and applied to the cell cycle data that motivated the problem.
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    Derivative-Free Optimization of Rate Parameters of Capsid Assembly Models from Bulk in Vitro Data (2017)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2017-08-14
    Description: The assembly of virus capsids proceeds by a complicated cascade of association and dissociation steps, the great majority of which cannot be directly experimentally observed. This has made capsid assembly a rich field for computational models, but there are substantial obstacles to model inference for such systems. Here, we describe progress on fitting kinetic rate constants defining capsid assembly models to experimental data, a difficult data-fitting problem because of the high computational cost of simulating assembly trajectories, the stochastic noise inherent to the models, and the limited and noisy data available for fitting. We evaluate the merits of data-fitting methods based on derivative-free optimization (DFO) relative to gradient-based methods used in prior work. We further explore the advantages of alternative data sources through simulation of a model of time-resolved mass spectrometry data, a technology for monitoring bulk capsid assembly that can be expected to provide much richer data than previously used static light scattering approaches. The results show that advances in both the data and the algorithms can improve model inference. More informative data sources lead to high-quality fits for all methods, but DFO methods show substantial advantages on less informative data sources that better represent current experimental practice.
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    cuBLASTP: Fine-Grained Parallelization of Protein Sequence Search on CPU+GPU (2017)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2017-08-14
    Description: BLAST, short for Basic Local Alignment Search Tool, is a ubiquitous tool used in the life sciences for pairwise sequence search. However, with the advent of next-generation sequencing (NGS), whether at the outset or downstream from NGS, the exponential growth of sequence databases is outstripping our ability to analyze the data. While recent studies have utilized the graphics processing unit (GPU) to speedup the BLAST algorithm for searching protein sequences (i.e., BLASTP), these studies use coarse-grained parallelism, where one sequence alignment is mapped to only one thread. Such an approach does not efficiently utilize the capabilities of a GPU, particularly due to the irregularity of BLASTP in both execution paths and memory-access patterns. To address the above shortcomings, we present a fine-grained approach to parallelize BLASTP, where each individual phase of sequence search is mapped to many threads on a GPU. This approach, which we refer to as cuBLASTP, reorders data-access patterns and reduces divergent branches of the most time-consuming phases (i.e., hit detection and ungapped extension). In addition, cuBLASTP optimizes the remaining phases (i.e., gapped extension and alignment with trace back) on a multicore CPU and overlaps their execution with the phases running on the GPU.
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    Identifying Cell Populations in Flow Cytometry Data Using Phenotypic Signatures (2017)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2017-08-14
    Description: Single-cell flow cytometry is a technology that measures the expression of several cellular markers simultaneously for a large number of cells. Identification of homogeneous cell populations, currently done by manual biaxial gating, is highly subjective and time consuming. To overcome the shortcomings of manual gating, automatic algorithms have been proposed. However, the performance of these methods highly depends on the shape of populations and the dimension of the data. In this paper, we have developed a time-efficient method that accurately identifies cellular populations. This is done based on a novel technique that estimates the initial number of clusters in high dimension and identifies the final clusters by merging clusters using their phenotypic signatures in low dimension. The proposed method is called SigClust . We have applied SigClust to four public datasets and compared it with five well known methods in the field. The results are promising and indicate higher performance and accuracy compared to similar approaches reported in literature.
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    Impact of Synaptic Localization and Subunit Composition of Ionotropic Glutamate Receptors on Synaptic Function: Modeling and Simulation Studies (2017)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2017-08-14
    Description: Ionotropic NMDA and AMPA glutamate receptors (iGluRs) play important roles in synaptic function under physiological and pathological conditions. iGluRs sub-synaptic localization and subunit composition are dynamically regulated by activity-dependent insertion and internalization. However, understanding the impact on synaptic transmission of changes in composition and localization of iGluRs is difficult to address experimentally. To address this question, we developed a detailed computational model of glutamatergic synapses, including spine and dendritic compartments, elementary models of subtypes of NMDA and AMPA receptors, glial glutamate transporters, intracellular calcium, and a calcium-dependent signaling cascade underlying the development of long-term potentiation (LTP). These synapses were distributed on a neuron model and numerical simulations were performed to assess the impact of changes in composition and localization (synaptic versus extrasynaptic) of iGluRs on synaptic transmission and plasticity following various patterns of presynaptic stimulation. In addition, the effects of various pharmacological compounds targeting NMDARs or AMPARs were determined. Our results showed that changes in NMDAR localization have a greater impact on synaptic plasticity than changes in AMPARs. Moreover, the results suggest that modulators of AMPA and NMDA receptors have differential effects on restoring synaptic plasticity under different experimental situations mimicking various human diseases.
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    Inferring MicroRNA-Disease Associations by Random Walk on a Heterogeneous Network with Multiple Data Sources (2017)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2017-08-14
    Description: Since the discovery of the regulatory function of microRNA (miRNA), increased attention has focused on identifying the relationship between miRNA and disease. It has been suggested that computational method is an efficient way to identify potential disease-related miRNAs for further confirmation using biological experiments. In this paper, we first highlighted three limitations commonly associated with previous computational methods. To resolve these limitations, we established disease similarity subnetwork and miRNA similarity subnetwork by integrating multiple data sources, where the disease similarity is composed of disease semantic similarity and disease functional similarity, and the miRNA similarity is calculated using the miRNA-target gene and miRNA-lncRNA (long non-coding RNA) associations. Then, a heterogeneous network was constructed by connecting the disease similarity subnetwork and the miRNA similarity subnetwork using the known miRNA-disease associations. We extended random walk with restart to predict miRNA-disease associations in the heterogeneous network. The leave-one-out cross-validation achieved an average area under the curve (AUC) of $0.8049$ across $341$ diseases and $476$ miRNAs. For five-fold cross-validation, our method achieved an AUC from $0.7970$ to $0.9249$ for $15$ human diseases. Case studies further demonstrated the feasibility of our method to discover potential miRNA-disease associations. An online service for prediction is freely available at http://ifmda.aliapp.com .
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    ISEA: Iterative Seed-Extension Algorithm for De Novo Assembly Using Paired-End Information and Insert Size Distribution (2017)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2017-08-14
    Description: The purpose of de novo assembly is to report more contiguous, complete, and less error prone contigs. Thanks to the advent of the next generation sequencing (NGS) technologies, the cost of producing high depth reads is reduced greatly. However, due to the disadvantages of NGS, de novo assembly has to face the difficulties brought by repeat regions, error rate, and low sequencing coverage in some regions. Although many de novo algorithms have been proposed to solve these problems, the de novo assembly still remains a challenge. In this article, we developed an iterative seed-extension algorithm for de novo assembly, called ISEA. To avoid the negative impact induced by error rate, ISEA utilizes reads overlap and paired-end information to correct error reads before assemblying. During extending seeds in a De Bruijn graph, ISEA uses an elaborately designed score function based on paired-end information and the distribution of insert size to solve the repeat region problem. By employing the distribution of insert size, the score function can also reduce the influence of error reads. In scaffolding, ISEA adopts a relaxed strategy to join contigs that were terminated for low coverage during the extension. The performance of ISEA was compared with six previous popular assemblers on four real datasets. The experimental results demonstrate that ISEA can effectively obtain longer and more accurate scaffolds.
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    Finite-Time Stability Analysis of Reaction-Diffusion Genetic Regulatory Networks with Time-Varying Delays (2017)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2017-08-14
    Description: This paper is concerned with the finite-time stability problem of the delayed genetic regulatory networks (GRNs) with reaction-diffusion terms under Dirichlet boundary conditions. By constructing a Lyapunov–Krasovskii functional including quad-slope integrations, we establish delay-dependent finite-time stability criteria by employing the Wirtinger-type integral inequality, Gronwall inequality, convex technique, and reciprocally convex technique. In addition, the obtained criteria are also reaction-diffusion-dependent. Finally, a numerical example is provided to illustrate the effectiveness of the theoretical results.
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    Pattern Classification of Instantaneous Cognitive Task-load Through GMM Clustering, Laplacian Eigenmap, and Ensemble SVMs (2017)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2017-08-14
    Description: The identification of the temporal variations in human operator cognitive task-load (CTL) is crucial for preventing possible accidents in human-machine collaborative systems. Recent literature has shown that the change of discrete CTL level during human-machine system operations can be objectively recognized using neurophysiological data and supervised learning technique. The objective of this work is to design subject-specific multi-class CTL classifier to reveal the complex unknown relationship between the operator's task performance and neurophysiological features by combining target class labeling, physiological feature reduction and selection, and ensemble classification techniques. The psychophysiological data acquisition experiments were performed under multiple human-machine process control tasks. Four or five target classes of CTL were determined by using a Gaussian mixture model and three human performance variables. By using Laplacian eigenmap, a few salient EEG features were extracted, and heart rates were used as the input features of the CTL classifier. Then, multiple support vector machines were aggregated via majority voting to create an ensemble classifier for recognizing the CTL classes. Finally, the obtained CTL classification results were compared with those of several existing methods. The results showed that the proposed methods are capable of deriving a reasonable number of target classes and low-dimensional optimal EEG features for individual human operator subjects.
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    Search for a Minimal Set of Parameters by Assessing the Total Optimization Potential for a Dynamic Model of a Biochemical Network (2017)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2017-08-14
    Description: Selecting an efficient small set of adjustable parameters to improve metabolic features of an organism is important for a reduction of implementation costs and risks of unpredicted side effects. In practice, to avoid the analysis of a huge combinatorial space for the possible sets of adjustable parameters, experience-, and intuition-based subsets of parameters are often chosen, possibly leaving some interesting counter-intuitive combinations of parameters unrevealed. The combinatorial scan of possible adjustable parameter combinations at the model optimization level is possible; however, the number of analyzed combinations is still limited. The total optimization potential (TOP) approach is proposed to assess the full potential for increasing the value of the objective function by optimizing all possible adjustable parameters. This seemingly unpractical combination of adjustable parameters allows assessing the maximum attainable value of the objective function and stopping the combinatorial space scanning when the desired fraction of TOP is reached and any further increase in the number of adjustable parameters cannot bring any reasonable improvement. The relation between the number of adjustable parameters and the reachable fraction of TOP is a valuable guideline in choosing a rational solution for industrial implementation. The TOP approach is demonstrated on the basis of two case studies.
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    Special Issue on “IEEE/ACM Transactions on Computational Biology and Bioinformatics” (2017)
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    Publication Date: 2017-08-14
    Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
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    Guest Editorial: Special Section on Biological Data Mining and Its Applications in Healthcare (2017)
    Institute of Electrical and Electronics Engineers (IEEE)
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    Publication Date: 2017-06-07
    Description: Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data—genomic sequences, DNA microarrays, protein interactions, biomedical images, disease pathways, etc. The rapid adoption of Electronic Health Records (EHRs) across healthcare systems, coupled with the capability of linking EHRs to research biorepositories, provides a unique opportunity for conducting large-scale Precision Medicine research. As a result, data mining techniques, for knowledge discovery and deriving data driven insights from various data sources, are increasingly important in modern biology and healthcare. The purpose of this special section is to bring together the researchers in bioinformatics, healthcare informatics, and data mining to share about their current research, and their visions on future directions.
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    Applications of Transductive Spectral Clustering Methods in a Military Medical Concussion Database (2017)
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    Publication Date: 2017-06-07
    Description: Traumatic brain injury (TBI) is one of the most common forms of neurotrauma that has affected more than 250,000 military service members over the last decade alone. While in battle, service members who experience TBI are at significant risk for the development of normal TBI symptoms, as well as risk for the development of psychological disorders such as Post-Traumatic Stress Disorder (PTSD). As such, these service members often require intense bouts of medication and therapy in order to resume full return-to-duty status. The primary aim of this study is to identify the relationship between the administration of specific medications and reductions in symptomology such as headaches, dizziness, or light-headedness. Service members diagnosed with mTBI and seen at the Concussion Restoration Care Center (CRCC) in Afghanistan were analyzed according to prescribed medications and symptomology. Here, we demonstrate that in such situations with sparse labels and small feature sets, classic analytic techniques such as logistic regression, support vector machines, naïve Bayes, random forest, decision trees, and k-nearest neighbor are not well suited for the prediction of outcomes. We attribute our findings to several issues inherent to this problem setting and discuss several advantages of spectral graph methods.
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    Bi-convex Optimization to Learn Classifiers from Multiple Biomedical Annotations (2017)
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    Publication Date: 2017-06-07
    Description: The problem of constructing classifiers from multiple annotators who provide inconsistent training labels is important and occurs in many application domains. Many existing methods focus on the understanding and learning of the crowd behaviors. Several probabilistic algorithms consider the construction of classifiers for specific tasks using consensus of multiple labelers annotations. These methods impose a prior on the consensus and develop an expectation-maximization algorithm based on logistic regression loss. We extend the discussion to the hinge loss commonly used by support vector machines. Our formulations form bi-convex programs that construct classifiers and estimate the reliability of each labeler simultaneously. Each labeler is associated with a reliability parameter, which can be a constant, or class-dependent, or varies for different examples. The hinge loss is modified by replacing the true labels by the weighted combination of labelers’ labels with reliabilities as weights. Statistical justification is discussed to motivate the use of linear combination of labels. In parallel to the expectation-maximization algorithm for logistic-based methods, efficient alternating algorithms are developed to solve the proposed bi-convex programs. Experimental results on benchmark datasets and three real-world biomedical problems demonstrate that the proposed methods either outperform or are competitive to the state of the art.
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    Towards Unsupervised Gene Selection: A Matrix Factorization Framework (2017)
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    Publication Date: 2017-06-07
    Description: The recent development of microarray gene expression techniques have made it possible to offer phenotype classification of many diseases. However, in gene expression data analysis, each sample is represented by quite a large number of genes, and many of them are redundant or insignificant to clarify the disease problem. Therefore, how to efficiently select the most useful genes has been becoming one of the most hot research topics in the gene expression data analysis. In this paper, a novel unsupervised two-stage coarse-fine gene selection method is proposed. In the first stage, we apply the kmeans algorithm to over-cluster the genes and discard some redundant genes. In the second stage, we select the most representative genes from the remaining ones based on matrix factorization. Finally the experimental results on several data sets are presented to show the effectiveness of our method.
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    Modeling Healthcare Quality via Compact Representations of Electronic Health Records (2017)
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    Publication Date: 2017-06-07
    Description: Increased availability of Electronic Health Record (EHR) data provides unique opportunities for improving the quality of health services. In this study, we couple EHRs with the advanced machine learning tools to predict three important parameters of healthcare quality. More specifically, we describe how to learn low-dimensional vector representations of patient conditions and clinical procedures in an unsupervised manner, and generate feature vectors of hospitalized patients useful for predicting their length of stay, total incurred charges, and mortality rates. In order to learn vector representations, we propose to employ state-of-the-art language models specifically designed for modeling co-occurrence of diseases and applied clinical procedures. The proposed model is trained on a large-scale EHR database comprising more than 35 million hospitalizations in California over a period of nine years. We compared the proposed approach to several alternatives and evaluated their effectiveness by measuring accuracy of regression and classification models used for three predictive tasks considered in this study. Our model outperformed the baseline models on all tasks, indicating a strong potential of the proposed approach for advancing quality of the healthcare system.
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    On the Complexity of Duplication-Transfer-Loss Reconciliation with Non-Binary Gene Trees (2017)
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    Publication Date: 2017-06-07
    Description: Duplication-Transfer-Loss (DTL) reconciliation has emerged as a powerful technique for studying gene family evolution in the presence of horizontal gene transfer. DTL reconciliation takes as input a gene family phylogeny and the corresponding species phylogeny, and reconciles the two by postulating speciation, gene duplication, horizontal gene transfer, and gene loss events. Efficient algorithms exist for finding optimal DTL reconciliations when the gene tree is binary. However, gene trees are frequently non-binary. With such non-binary gene trees, the reconciliation problem seeks to find a binary resolution of the gene tree that minimizes the reconciliation cost. Given the prevalence of non-binary gene trees, many efficient algorithms have been developed for this problem in the context of the simpler Duplication-Loss (DL) reconciliation model. Yet, no efficient algorithms exist for DTL reconciliation with non-binary gene trees and the complexity of the problem remains unknown. In this work, we resolve this open question by showing that the problem is, in fact, NP-hard. Our reduction applies to both the dated and undated formulations of DTL reconciliation. By resolving this long-standing open problem, this work will spur the development of both exact and heuristic algorithms for this important problem.
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    Calcium Ion Fluctuations Alter Channel Gating in a Stochastic Luminal Calcium Release Site Model (2017)
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    Publication Date: 2017-06-07
    Description: Stochasticity and small system size effects in complex biochemical reaction networks can greatly alter transient and steady-state system properties. A common approach to modeling reaction networks, which accounts for system size, is the chemical master equation that governs the dynamics of the joint probability distribution for molecular copy number. However, calculation of the stationary distribution is often prohibitive, due to the large state-space associated with most biochemical reaction networks. Here, we analyze a network representing a luminal calcium release site model and investigate to what extent small system size effects and calcium fluctuations, driven by ion channel gating, influx and diffusion, alter steady-state ion channel properties including open probability. For a physiological ion channel gating model and number of channels, the state-space may be between approximately $10^6-10^8$ elements, and a novel modified block power method is used to solve the associated dominant eigenvector problem required to calculate the stationary distribution. We demonstrate that both small local cytosolic domain volume and a small number of ion channels drive calcium fluctuations that result in deviation from the corresponding model that neglects small system size effects.
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    Sorting Circular Permutations by Super Short Reversals (2017)
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    Publication Date: 2017-06-07
    Description: We consider the problem of sorting a circular permutation by super short reversals (i.e., reversals of length at most 2), a problem that finds application in comparative genomics. Polynomial-time solutions to the unsigned version of this problem are known, but the signed version remained open. In this paper, we present the first polynomial-time solution to the signed version of this problem. Moreover, we perform experiments for inferring phylogenies of two different groups of bacterial species and compare our results with the phylogenies presented in previous works. Finally, to facilitate phylogenetic studies based on the methods studied in this paper, we present a web tool for rearrangement-based phylogenetic inference using short operations, such as super short reversals.
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    Guest Editors’ Introduction to the Special Section on Bioinformatics Research and Applications (2017)
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    Publication Date: 2017-06-07
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    Efficient Constant-Time Complexity Algorithm for Stochastic Simulation of Large Reaction Networks (2017)
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    Publication Date: 2017-06-07
    Description: Exact stochastic simulation is an indispensable tool for a quantitative study of biochemical reaction networks. The simulation realizes the time evolution of the model by randomly choosing a reaction to fire and update the system state according to a probability that is proportional to the reaction propensity. Two computationally expensive tasks in simulating large biochemical networks are the selection of next reaction firings and the update of reaction propensities due to state changes. We present in this work a new exact algorithm to optimize both of these simulation bottlenecks. Our algorithm employs the composition-rejection on the propensity bounds of reactions to select the next reaction firing. The selection of next reaction firings is independent of the number reactions while the update of propensities is skipped and performed only when necessary. It therefore provides a favorable scaling for the computational complexity in simulating large reaction networks. We benchmark our new algorithm with the state of the art algorithms available in literature to demonstrate its applicability and efficiency.
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    Leveraging FPGAs for Accelerating Short Read Alignment (2017)
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    Publication Date: 2017-06-07
    Description: One of the key challenges facing genomics today is how to efficiently analyze the massive amounts of data produced by next-generation sequencing platforms. With general-purpose computing systems struggling to address this challenge, specialized processors such as the Field-Programmable Gate Array (FPGA) are receiving growing interest. The means by which to leverage this technology for accelerating genomic data analysis is however largely unexplored. In this paper, we present a runtime reconfigurable architecture for accelerating short read alignment using FPGAs. This architecture exploits the reconfigurability of FPGAs to allow the development of fast yet flexible alignment designs. We apply this architecture to develop an alignment design which supports exact and approximate alignment with up to two mismatches. Our design is based on the FM-index, with optimizations to improve the alignment performance. In particular, the $n$ -step FM-index, index oversampling, a seed-and-compare stage, and bi-directional backtracking are included. Our design is implemented and evaluated on a 1U Maxeler MPC-X2000 dataflow node with eight Altera Stratix-V FPGAs. Measurements show that our design is 28 times faster than Bowtie2 running with 16 threads on dual Intel Xeon E5-2640 CPUs, and nine times faster than Soap3-dp running on an NVIDIA Tesla C2070 GPU.
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    PCID: A Novel Approach for Predicting Disease Comorbidity by Integrating Multi-Scale Data (2017)
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    Publication Date: 2017-06-07
    Description: Disease comorbidity is the presence of one or more diseases along with a primary disorder, which causes additional pain to patients and leads to the failure of standard treatments compared with single diseases. Therefore, the identification of potential comorbidity can help prevent those comorbid diseases when treating a primary disease. Unfortunately, most of current known disease comorbidities are discovered occasionally in clinic, and our knowledge about comorbidity is far from complete. Despite the fact that many efforts have been made to predict disease comorbidity, the prediction accuracy of existing computational approaches needs to be improved. By investigating the factors underlying disease comorbidity, e.g., mutated genes and rewired protein-protein interactions (PPIs), we here present a novel algorithm to predict disease comorbidity by integrating multi-scale data ranging from genes to phenotypes. Benchmark results on real data show that our approach outperforms existing algorithms, and some of our novel predictions are validated with those reported in literature, indicating the effectiveness and predictive power of our approach. In addition, we identify some pathway and PPI patterns that underlie the co-occurrence between a primary disease and certain disease classes, which can help explain how the comorbidity is initiated from molecular perspectives.
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    Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization (2017)
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    Publication Date: 2017-06-07
    Description: Experimental determination of drug-target interactions is expensive and time-consuming. Therefore, there is a continuous demand for more accurate predictions of interactions using computational techniques. Algorithms have been devised to infer novel interactions on a global scale where the input to these algorithms is a drug-target network (i.e., a bipartite graph where edges connect pairs of drugs and targets that are known to interact). However, these algorithms had difficulty predicting interactions involving new drugs or targets for which there are no known interactions (i.e., “orphan” nodes in the network). Since data usually lie on or near to low-dimensional non-linear manifolds, we propose two matrix factorization methods that use graph regularization in order to learn such manifolds. In addition, considering that many of the non-occurring edges in the network are actually unknown or missing cases, we developed a preprocessing step to enhance predictions in the “new drug” and “new target” cases by adding edges with intermediate interaction likelihood scores. In our cross validation experiments, our methods achieved better results than three other state-of-the-art methods in most cases. Finally, we simulated some “new drug” and “new target” cases and found that GRMF predicted the left-out interactions reasonably well.
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    Protein Complex Detection via Effective Integration of Base Clustering Solutions and Co-Complex Affinity Scores (2017)
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    Publication Date: 2017-06-07
    Description: With the increasing availability of protein interaction data, various computational methods have been developed to predict protein complexes. However, different computational methods may have their own advantages and limitations. Ensemble clustering has thus been studied to minimize the potential bias and risk of individual methods and generate prediction results with better coverage and accuracy. In this paper, we extend the traditional ensemble clustering by taking into account the co-complex affinity scores and present an Ensem ble H ierarchical C lustering framework (EnsemHC) to detect protein complexes. First, we construct co-cluster matrices by integrating the clustering results with the co-complex evidences. Second, we sum up the constructed co-cluster matrices to derive a final ensemble matrix via a novel iterative weighting scheme. Finally, we apply the hierarchical clustering to generate protein complexes from the final ensemble matrix. Experimental results demonstrate that our EnsemHC performs better than its base clustering methods and various existing integrative methods. In addition, we also observed that integrating the clusters and co-complex affinity scores from different data sources will improve the prediction performance, e.g., integrating the clusters from TAP data and co-complex affinities from binary PPI data achieved the best performance in our experiments.
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    Robust Reachability of Boolean Control Networks (2017)
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    Publication Date: 2017-06-07
    Description: Boolean networks serve a powerful tool in analysis of genetic regulatory networks since it emphasizes the fundamental principles and establishes a nature framework for capturing the dynamics of regulation of cellular states. In this paper, the robust reachability of Boolean control networks is investigated by means of semi-tensor product. Necessary and sufficient conditions for the robust reachability of Boolean control networks are provided, in which control inputs relying on disturbances or not are considered, respectively. Besides, the corresponding control algorithms are developed for these two cases. A reduced model of the lac operon in the Escherichia coli is presented to show the effectiveness of the presented results.
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    txCoords: A Novel Web Application for Transcriptomic Peak Re-Mapping (2017)
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    Publication Date: 2017-06-07
    Description: Since the development of new technologies such as RIP-Seq and m 6 A-seq, peak calling has become an important step in transcriptomic sequencing data analysis. However, many of the reported genomic coordinates of transcriptomic peaks are incorrect owing to negligence of the introns. There is currently a lack of a convenient tool to address this problem. Here, we present txCoords, a novel and easy-to-use web application for transcriptomic peak re-mapping. txCoords can be used to correct the incorrectly reported transcriptomic peaks and retrieve the true sequences. It also supports visualization of the re-mapped peaks in a schematic figure or from the UCSC Genome Browser. Our web server is freely available at http://www.bioinfo.tsinghua.edu.cn/txCoords .
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    Prognosis of Clinical Outcomes with Temporal Patterns and Experiences with One Class Feature Selection (2017)
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    Publication Date: 2017-06-10
    Description: Accurate prognosis of outcome events, such as clinical procedures or disease diagnosis, is central in medicine. The emergence of longitudinal clinical data, like the Electronic Health Records (EHR), represents an opportunity to develop automated methods for predicting patient outcomes. However, these data are highly dimensional and very sparse, complicating the application of predictive modeling techniques. Further, their temporal nature is not fully exploited by current methods, and temporal abstraction was recently used which results in symbolic time intervals representation. We present Maitreya, a framework for the prediction of outcome events that leverages these symbolic time intervals. Using Maitreya, learn predictive models based on the temporal patterns in the clinical records that are prognostic markers and use these markers to train predictive models for eight clinical procedures. In order to decrease the number of patterns that are used as features, we propose the use of three one class feature selection methods. We evaluate the performance of Maitreya under several parameter settings, including the one-class feature selection, and compare our results to that of atemporal approaches. In general, we found that the use of temporal patterns outperformed the atemporal methods, when representing the number of pattern occurrences.
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    Front Cover (2017)
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    CALL FOR PAPERS IEEE Geoscience and Remote Sensing Magazine (2017)
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    Staff Listing (2017)
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    Taking Optimal Advantage of Fine Spatial Resolution: Promoting partial image reconstruction for the morphological analysis of very-high-resolution images (2017)
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    Publication Date: 2017-06-14
    Description: Diverse sensor technologies have allowed us to measure different aspects of objects on Earth's surface [such as spectral characteristics in hyperspectral images and height in light detection and ranging (LiDAR) data] with increasing spectral and spatial resolutions. Remote-sensing images of very high geometrical resolution can provide a precise and detailed representation of the monitored scene. Thus, the spatial information is fundamental for many applications. Morphological profiles (MPs) and attribute profiles (APs) have been widely used to model the spatial information of very-high-resolution (VHR) remote-sensing images. MPs are obtained by computing a sequence of morphological operators based on geodesic reconstruction. However, both morphological operators based on geodesic reconstruction and attribute filters (AFs) are connected filters and, hence, suffer the problem of leakage (i.e., regions related to different structures in the image that happen to be connected by spurious links are considered as a single object). Objects expected to disappear at a given stage remain present when they connect with other objects in the image. Consequently, the attributes of small objects are mixed with their larger connected objects, leading to poor performances on postapplications (e.g., classification).
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    Hyperspectral and Multispectral Data Fusion: A comparative review of the recent literature (2017)
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    Publication Date: 2017-06-14
    Description: In recent years, enormous efforts have been made to design image-processing algorithms to enhance the spatial resolution of hyperspectral (HS) imagery. One of the most commonly addressed problems is the fusion of HS data with higher spatial resolution multispectral (MS) data. Various techniques have been proposed to solve this data-fusion problem based on different theories, including component substitution (CS), multiresolution analysis (MRA), spectral unmixing, and Bayesian probability. This article presents a comparative review of those HS-MS fusion techniques with extensive experiments. Ten state-of-the-art HS-MS fusion methods are compared by assessing their fusion performance both quantitatively and visually. Eight data sets featuring different geographical and sensor characteristics are used in the experiments to evaluate the generalizability and versatility of the fusion algorithms. To maximize the fairness and transparency of this comparison, publicly available source codes are used, and parameters are individually tuned for maximum performance.
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    Airborne Measurements of Snow Thickness: Using ultrawide-band frequency-modulated-continuous-wave radars (2017)
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    Publication Date: 2017-06-14
    Description: Frequency-modulated-continuous-wave (FM-CW) radar has been used extensively for the airborne measurement of snow thickness over sea ice and the mapping of annual accumulation over land ice. In contrast to conventional in situ measurements, FM-CW radar, when operated onboard an airborne platform, can be a useful tool for widearea surveys of snow deposition. Since the early 2000s, the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas (KU) has designed, developed, and deployed airborne ultrawide-band (UWB) FM-CW radars, called Snow Radars, on National Science Foundation (NSF)-, NASA-, Naval Research Laboratory (NRL)-, and Alfred Wegener Institute (AWI)-provided aircraft in both Arctic and Antarctic regions and generated a large amount of snow data products. In addition to the significant standalone value of the snow-thickness measurements, these data are being used in estimating Arctic sea ice thickness, which is a key variable in the study of atmosphere-ocean-ice interactions. This article provides a review of snow remote sensing techniques with airborne FM-CW radars to document the operating principle, design, and evolution of CReSIS' UWB FM-CW radars and discuss and promote understanding of the advantages and limitations associated with these systems.
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    Table of Contents (2017)
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    Welcome to the June 2017 Issue [From the Editor] (2017)
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    IEEE GRSS Continues Its Mission [President's Message] (2017)
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    GRSS Members Elevated to Senior Member in February 2017 [GRSS Member Highlights] (2017)
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    [Calendar] (2017)
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    Ground Station Operations Training Hosted by Eastern North Carolina Chapter Chapter [Education] (2017)
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    CPGIS 2017 (2017)
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    The ESA's Earth Observation Open Science Program [Space Agencies] (2017)
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    Publication Date: 2017-06-14
    Description: The world of Earth observation (EO) data is rapidly changing, driven by exponential advances in sensor and digital technologies. Recent decades have seen the development of extraordinary new ways of collecting, storing, manipulating, and transmitting data that are radically transforming the way we conduct and organize science. This convergence of technologies creates new challenges for EO scientists and data and software providers to fully exploit large amounts of multivariate data from diverse sources. At the same time, these technological trends also generate huge opportunities to better understand our planet and turn big data into new types of information services. This article briefly describes some of the elements of the European Space Agency's (ESA) EO Open Science program, which aims to enable the digital transformation of the EO community and make the most of the large, complex, and diverse data delivered by the new generation of EO missions, such as the Copernicus Sentinels.
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    Promoting the Success of Women in Engineering Through Affinity Groups [Women in GRS] (2017)
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    Prediction and Validation of Disease Genes Using HeteSim Scores (2017)
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    Publication Date: 2017-06-07
    Description: Deciphering the gene disease association is an important goal in biomedical research. In this paper, we use a novel relevance measure, called HeteSim, to prioritize candidate disease genes. Two methods based on heterogeneous networks constructed using protein-protein interaction, gene-phenotype associations, and phenotype-phenotype similarity, are presented. In HeteSim_MultiPath (HSMP), HeteSim scores of different paths are combined with a constant that dampens the contributions of longer paths. In HeteSim_SVM (HSSVM), HeteSim scores are combined with a machine learning method. The 3-fold experiments show that our non-machine learning method HSMP performs better than the existing non-machine learning methods, our machine learning method HSSVM obtains similar accuracy with the best existing machine learning method CATAPULT. From the analysis of the top 10 predicted genes for different diseases, we found that HSSVM avoid the disadvantage of the existing machine learning based methods, which always predict similar genes for different diseases. The data sets and Matlab code for the two methods are freely available for download at http://lab.malab.cn/data/HeteSim/index.jsp .
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    A Heterogeneous Network Based Method for Identifying GBM-Related Genes by Integrating Multi-Dimensional Data (2017)
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    Publication Date: 2017-06-07
    Description: The emergence of multi-dimensional data offers opportunities for more comprehensive analysis of the molecular characteristics of human diseases and therefore improving diagnosis, treatment, and prevention. In this study, we proposed a heterogeneous network based method by integrating multi-dimensional data (HNMD) to identify GBM-related genes. The novelty of the method lies in that the multi-dimensional data of GBM from TCGA dataset that provide comprehensive information of genes, are combined with protein-protein interactions to construct a weighted heterogeneous network, which reflects both the general and disease-specific relationships between genes. In addition, a propagation algorithm with resistance is introduced to precisely score and rank GBM-related genes. The results of comprehensive performance evaluation show that the proposed method significantly outperforms the network based methods with single-dimensional data and other existing approaches. Subsequent analysis of the top ranked genes suggests they may be functionally implicated in GBM, which further corroborates the superiority of the proposed method. The source code and the results of HNMD can be downloaded from the following URL: http://bioinformatics.ustc.edu.cn/hnmd/ .
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    Classical Mechanics Approach Applied to Analysis of Genetic Oscillators (2017)
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    Publication Date: 2017-06-07
    Description: Biological oscillators present a fundamental part of several regulatory mechanisms that control the response of various biological systems. Several analytical approaches for their analysis have been reported recently. They are, however, limited to only specific oscillator topologies and/or to giving only qualitative answers, i.e., is the dynamics of an oscillator given the parameter space oscillatory or not. Here, we present a general analytical approach that can be applied to the analysis of biological oscillators. It relies on the projection of biological systems to classical mechanics systems. The approach is able to provide us with relatively accurate results in the meaning of type of behavior system reflects (i.e., oscillatory or not) and periods of potential oscillations without the necessity to conduct expensive numerical simulations. We demonstrate and verify the proposed approach on three different implementations of amplified negative feedback oscillator.
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    A Modified Multiple Alignment Fast Fourier Transform with Higher Efficiency (2017)
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    Publication Date: 2017-06-07
    Description: Multiple sequence alignment (MSA) is the most common task in bioinformatics. Multiple alignment fast Fourier transform (MAFFT) is the fastest MSA program among those the accuracy of the resulting alignments can be comparable with the most accurate MSA programs. In this paper, we modify the correlation computation scheme of the MAFFT for further efficiency improvement in three aspects. First, novel complex number based amino acid and nucleotide expressions are utilized in the modified correlation. Second, linear convolution with a limitation is proposed for computing the correlation of amino acid and nucleotide sequences. Third, we devise a fast Fourier transform (FFT) algorithm for computing linear convolution. The FFT algorithm is based on conjugate pair split-radix FFT and does not require the permutation of order, and it is new as only real parts of the final outputs are required. Simulation results show that the speed of the modified scheme is 107.58 to 365.74 percent faster than that of the original MAFFT for one execution of the function Falign() of MAFFT, indicating its faster realization.
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    Searching Genome-Wide Multi-Locus Associations for Multiple Diseases Based on Bayesian Inference (2017)
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    Publication Date: 2017-06-07
    Description: Taking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unraveling complex relationships between genotypes and phenotypes. Current multi-locus-based methods are insufficient to detect interactions with diverse genetic effects on multifarious diseases. Also, statistic tests for high-order epistasis ( $\geq 2$ SNPs) raise huge computational and analytical challenges because the computation increases exponentially as the growth of the cardinality of SNPs combinations. In this paper, we provide a simple, fast and powerful method, named DAM, using Bayesian inference to detect genome-wide multi-locus epistatic interactions in multiple diseases. Experimental results on simulated data demonstrate that our method is powerful and efficient. We also apply DAM on two GWAS datasets from WTCCC, i.e . , Rheumatoid Arthritis and Type 1 Diabetes, and identify some novel findings. Therefore, we believe that our method is suitable and efficient for the full-scale analysis of multi-disease-related interactions in GWASs.
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    An Effective Computational Method Incorporating Multiple Secondary Structure Predictions in Topology Determination for Cryo-EM Images (2017)
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    Publication Date: 2017-06-07
    Description: A key idea in de novo modeling of a medium-resolution density image obtained from cryo-electron microscopy is to compute the optimal mapping between the secondary structure traces observed in the density image and those predicted on the protein sequence. When secondary structures are not determined precisely, either from the image or from the amino acid sequence of the protein, the computational problem becomes more complex. We present an efficient method that addresses the secondary structure placement problem in presence of multiple secondary structure predictions and computes the optimal mapping. We tested the method using 12 simulated images from α-proteins and two Cryo-EM images of α-β proteins. We observed that the rank of the true topologies is consistently improved by using multiple secondary structure predictions instead of a single prediction. The results show that the algorithm is robust and works well even when errors/misses in the predicted secondary structures are present in the image or the sequence. The results also show that the algorithm is efficient and is able to handle proteins with as many as 33 helices.
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    Genomic Distance with High Indel Costs (2017)
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    Publication Date: 2017-06-07
    Description: We determine complexity of computing the DCJ-indel distance, when DCJ and indel operations have distinct constant costs, by showing an exact formula that can be computed in linear time for any choice of (constant) costs for DCJ and indel operations. We additionally consider the problem of triangular inequality disruption and propose an algorithmically efficient correction on each member of the family of DCJ-indel.
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    A Profile of Remote-Sensing Activities in the Kolkata Chapter 2015-2016 [Chapters] (2017)
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    Remote Sensing Code Library [Software and Data Sets] (2017)
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    IEEE GRSS Malaysia Chapter [Chapters] (2017)
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    IEEE Geoscience and Remote Sensing Society?s 2016 Best Reviewers [GRSS Member Highlights] (2017)
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    Publication Date: 2017-06-14
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    From Protein Sequence to Protein Function via Multi-Label Linear Discriminant Analysis (2017)
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    Publication Date: 2017-06-07
    Description: Sequence describes the primary structure of a protein, which contains important structural, characteristic, and genetic information and thereby motivates many sequence-based computational approaches to infer protein function. Among them, feature-base approaches attract increased attention because they make prediction from a set of transformed and more biologically meaningful sequence features. However, original features extracted from sequence are usually of high dimensionality and often compromised by irrelevant patterns, therefore dimension reduction is necessary prior to classification for efficient and effective protein function prediction. A protein usually performs several different functions within an organism, which makes protein function prediction a multi-label classification problem. In machine learning, multi-label classification deals with problems where each object may belong to more than one class. As a well-known feature reduction method, linear discriminant analysis (LDA) has been successfully applied in many practical applications. It, however, by nature is designed for single-label classification , in which each object can belong to exactly one class. Because directly applying LDA in multi-label classification causes ambiguity when computing scatters matrices, we apply a new Multi-label Linear Discriminant Analysis (MLDA) approach to address this problem and meanwhile preserve powerful classification capability inherited from classical LDA. We further extend MLDA by $\ell _1$ -normalization to overcome the problem of over-counting data points with multiple labels. In addition, we incorporate biological network data using Laplacian embedding into our method, and assess the reliability of predicted putative functions. Extensive empirical evaluations demonstrate pro- ising results of our methods.
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    Efficient Approach to Correct Read Alignment for Pseudogene Abundance Estimates (2017)
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    Publication Date: 2017-06-07
    Description: RNA-Sequencing has been the leading technology to quantify expression of thousands of genes simultaneously. The data analysis of an RNA-Seq experiment starts from aligning short reads to the reference genome/transcriptome or reconstructed transcriptome. However, current aligners lack the sensitivity to distinguish reads that come from homologous regions of an genome. One group of these homologies is the paralog pseudogenes. Pseudogenes arise from duplication of a set of protein coding genes, and have been considered as degraded paralogs in the genome due to their lost of functionality. Recent studies have provided evidence to support their novel regulatory roles in biological processes. With the growing interests in quantifying the expression level of pseudogenes at different tissues or cell lines, it is critical to have a sensitive method that can correctly align ambiguous reads and accurately estimate the expression level among homologous genes. Previously in PseudoLasso, we proposed a linear regression approach to learn read alignment behaviors, and to leverage this knowledge for abundance estimation and alignment correction. In this paper, we extend the work of PseudoLasso by grouping the homologous genomic regions into different communities using a community detection algorithm, followed by building a linear regression model separately for each community. The results show that this approach is able to retain the same accuracy as PseudoLasso. By breaking the genome into smaller homologous communities, the running time is improved from quadratic growth to linear with respect to the number of genes.
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    RNA Visualization: Relevance and the Current State-of-the-Art Focusing on Pseudoknots (2017)
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    Publication Date: 2017-06-07
    Description: RNA visualization is crucial in order to understand the relationship that exists between RNA structure and its function, as well as the development of better RNA structure prediction algorithms. However, in the context of RNA visualization, one key structure remains difficult to visualize: Pseudoknots. Pseudoknots occur in RNA folding when two secondary structural components form base-pairs between them. The three-dimensional nature of these components makes them challenging to visualize in two-dimensional media, such as print media or screens. In this review, we focus on the advancements that have been made in the field of RNA visualization in two-dimensional media in the past two decades. The review aims at presenting all relevant aspects of pseudoknot visualization. We start with an overview of several pseudoknotted structures and their relevance in RNA function. Next, we discuss the theoretical basis for RNA structural topology classification and present RNA classification systems for both pseudoknotted and non-pseudoknotted RNAs. Each description of RNA classification system is followed by a discussion of the software tools and algorithms developed to date to visualize RNA, comparing the different tools’ strengths and shortcomings.
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    PIERS 2017 (2017)
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    The China GEO Data Center: Bringing order to open Earth-observation data (2017)
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    Publication Date: 2017-06-14
    Description: Numerous remote-sensing devices observe the Earth every day, generating large amounts of remotesensing images and metadata. The extensive Earth obser vation (EO) data made available on the Internet by different EO agencies in multiple countries pose challenges for integration, storage, access, and collaborative applications. The capacity to explore and utilize all these heterogeneous EO data from across the world is very limited. In this article, we introduce the China Group on Earth Observations (GEO) Data Center, which is devoted to the efficient integration of EO data on the Internet and opening China's EO data to the world. We describe the infrastructure of the China GEO Data Center and discuss the design and implementation of the China Global Earth Observation System of Systems (GEOSS). We demonstrate the use of the GEO discovery and access broker (DAB) to harvest the large amounts of open EO data brokered by the GEO from different agencies around the world. We describe a data model we have designed to express the relations among EO data and capture the implicit semantic knowledge in EO data through rules (a model implemented using the Apache Jena framework). Finally, we describe the mechanisms for opening China's EO data to the world.
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    ARSI-KEO (2017)
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    GRSM-HSI (2017)
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    IEEE Collabratec filler (2017)
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    Description: Advertisement, IEEE.
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    IEEE Connecting filler (2017)
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    Guest Editor's Introduction: Selected Papers from ACM-BCB 2014 (2017)
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    Publication Date: 2017-10-11
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    Unconstrained Diameters for Deep Coalescence (2017)
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    Publication Date: 2017-10-11
    Description: The minimizing-deep-coalescence (MDC) approach infers a median (species) tree for a given set of gene trees under the deep coalescence cost. This cost accounts for the minimum number of deep coalescences needed to reconcile a gene tree with a species tree where the leaf-genes are mapped to the leaf-species through a function called leaf labeling. In order to better understand the MDC approach we investigate here the diameter of a gene tree, which is an important property of the deep coalescence cost. This diameter is the maximal deep coalescence costs for a given gene tree under all leaf labelings for each possible species tree topology. While we prove that this diameter is generally infinite, this result relies on the diameter’s unrealistic assumption that species trees can be of infinite size. Providing a more practical definition, we introduce a natural extension of the gene tree diameter that constrains the species tree size by a given constant. For this new diameter, we describe an exact formula, present a complete classification of the trees yielding this diameter, derive formulas for its mean and variance, and demonstrate its ability using comparative studies.
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    A Sparse Learning Framework for Joint Effect Analysis of Copy Number Variants (2017)
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    Publication Date: 2017-10-11
    Description: Copy number variants (CNVs), including large deletions and duplications, represent an unbalanced change of DNA segments. Abundant in human genomes, CNVs contribute to a large proportion of human genetic diversity, with impact on many human phenotypes. Although recent advances in genetic studies have shed light on the impact of individual CNVs on different traits, the analysis of joint effect of multiple interactive CNVs lags behind from many perspectives. A primary reason is that the large number of CNV combinations and interactions in the human genome make it computationally challenging to perform such joint analysis. To address this challenge, we developed a novel sparse learning framework that combines sparse learning with biological networks to identify interacting CNVs with joint effect on particular traits. We showed that our approach performs well in identifying CNVs with joint phenotypic effect using simulated data. Applied to a real human genomic dataset from the 1,000 Genomes Project, our approach identified multiple CNVs that collectively contribute to population differentiation. We found a set of multiple CNVs that have joint effect in different populations, and affect gene expression differently in distinct populations. These results provided a collection of CNVs that likely have downstream biomedical implications in individuals from diverse population backgrounds.
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    Improving Identification of Key Players in Aging via Network De-Noising and Core Inference (2017)
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    Publication Date: 2017-10-11
    Description: Current “ground truth” knowledge about human aging has been obtained by transferring aging-related knowledge from well-studied model species via sequence homology or by studying human gene expression data. Since proteins function by interacting with each other, analyzing protein-protein interaction (PPI) networks in the context of aging is promising. Unlike existing static network research of aging, since cellular functioning is dynamic , we recently integrated the static human PPI network with aging-related gene expression data to form dynamic , age-specific networks. Then, we predicted as key players in aging those proteins whose network topologies significantly changed with age. Since current networks are noisy , here, we use link prediction to de-noise the human network and predict improved key players in aging from the de-noised data. Indeed, de-noising gives more significant overlap between the predicted data and the “ground truth” aging-related data. Yet, we obtain novel predictions, which we validate in the literature. Also, we improve the predictions by an alternative strategy: removing “redundant” edges from the age-specific networks and using the resulting age-specific network “cores” to study aging. We produce new knowledge from dynamic networks encompassing multiple data types, via network de-noising or core inference, complementing the existing knowledge obtained from sequence or expression data.
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    An Annotation Agnostic Algorithm for Detecting Nascent RNA Transcripts in GRO-Seq (2017)
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    Publication Date: 2017-10-11
    Description: We present a fast and simple algorithm to detect nascent RNA transcription in global nuclear run-on sequencing (GRO-seq). GRO-seq is a relatively new protocol that captures nascent transcripts from actively engaged polymerase, providing a direct read-out on bona fide transcription. Most traditional assays, such as RNA-seq, measure steady state RNA levels which are affected by transcription, post-transcriptional processing, and RNA stability. GRO-seq data, however, presents unique analysis challenges that are only beginning to be addressed. Here, we describe a new algorithm, Fast Read Stitcher (FStitch), that takes advantage of two popular machine-learning techniques, hidden Markov models and logistic regression, to classify which regions of the genome are transcribed. Given a small user-defined training set, our algorithm is accurate, robust to varying read depth, annotation agnostic, and fast. Analysis of GRO-seq data without a priori need for annotation uncovers surprising new insights into several aspects of the transcription process.
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    IEEE What + If filler (2017)
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    IEEE Access filler (2017)
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    Learning Parameter-Advising Sets for Multiple Sequence Alignment (2017)
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    Publication Date: 2017-10-11
    Description: While the multiple sequence alignment output by an aligner strongly depends on the parameter values used for the alignment scoring function (such as the choice of gap penalties and substitution scores), most users rely on the single default parameter setting provided by the aligner. A different parameter setting, however, might yield a much higher-quality alignment for the specific set of input sequences. The problem of picking a good choice of parameter values for specific input sequences is called parameter advising . A parameter advisor has two ingredients: (i) a set of parameter choices to select from, and (ii) an estimator that provides an estimate of the accuracy of the alignment computed by the aligner using a parameter choice. The parameter advisor picks the parameter choice from the set whose resulting alignment has highest estimated accuracy. In this paper, we consider for the first time the problem of learning the optimal set of parameter choices for a parameter advisor that uses a given accuracy estimator. The optimal set is one that maximizes the expected true accuracy of the resulting parameter advisor, averaged over a collection of training data. While we prove that learning an optimal set for an advisor is NP-complete, we show there is a natural approximation algorithm for this problem, and prove a tight bound on its approximation ratio. Experiments with an implementation of this approximation algorithm on biological benchmarks, using various accuracy estimators from the literature, show it finds sets for advisors that are surprisingly close to optimal. Furthermore, the resulting parameter advisors are significantly more accurate in practice than simply aligning with a single default parameter choice.
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    Pathway Analysis with Signaling Hypergraphs (2017)
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    Publication Date: 2017-10-11
    Description: Signaling pathways play an important role in the cell’s response to its environment. Signaling pathways are often represented as directed graphs, which are not adequate for modeling reactions such as complex assembly and dissociation, combinatorial regulation, and protein activation/inactivation. More accurate representations such as directed hypergraphs remain underutilized. In this paper, we present an extension of a directed hypergraph that we call a signaling hypergraph. We formulate a problem that asks what proteins and interactions must be involved in order to stimulate a specific response downstream of a signaling pathway. We relate this problem to computing the shortest acyclic $B$ -hyperpath in a signaling hypergraph—an NP-hard problem—and present a mixed integer linear program to solve it. We demonstrate that the shortest hyperpaths computed in signaling hypergraphs are far more informative than shortest paths, Steiner trees, and subnetworks containing many short paths found in corresponding graph representations. Our results illustrate the potential of signaling hypergraphs as an improved representation of signaling pathways and motivate the development of novel hypergraph algorithms.
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