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TOC Alert for Publication# 8857 2017April 20<![CDATA[Editorial from the New Editor-in-Chief]]>14225125131<![CDATA[Guest Editorial for Special Section on BIBM 2014]]>14225225380<![CDATA[Discovering Protein-DNA Binding Cores by Aligned Pattern Clustering]]>1,600x) than that of its contemporaries, discovering candidates that do not co-occur as one-to-one associated patterns in the raw data. Availability: http://www.pami.uwaterloo.ca/~ealee/files/tcbbPnDna2015/Release.zip.]]>1422542631809<![CDATA[Multi-View Clustering of Microbiome Samples by Robust Similarity Network Fusion and Spectral Clustering]]>142264271414<![CDATA[Optimizing Analytical Depth and Cost Efficiency of IEF-LC/MS Proteomics]]>142272281717<![CDATA[Analysis of Organization of the Interactome Using Dominating Sets: A Case Study on Cell Cycle Interaction Networks]]>142282289483<![CDATA[Muscle Tissue Labeling of Human Lower Limb in Multi-Channel mDixon MR Imaging: Concepts and Applications]]>1422902991414<![CDATA[Improving Recognition of Antimicrobial Peptides and Target Selectivity through Machine Learning and Genetic Programming]]>142300313855<![CDATA[Guest Editors Introduction to the Special Section on ISBRA 2014]]>14231431592<![CDATA[A Two-Phase Improved Correlation Method for Automatic Particle Selection in Cryo-EM]]>1423163251249<![CDATA[An Approach for Peptide Identification by De Novo Sequencing of Mixture Spectra]]>de novo sequencing problem mathematically, and propose a dynamic programming algorithm for the problem. Additionally, we use both simulated and real mixture spectra data sets to verify the merits of the proposed algorithm.]]>142326336562<![CDATA[NovoExD: <italic>De novo</italic> Peptide Sequencing for ETD/ECD Spectra]]>De novo peptide sequencing using tandem mass spectrometry (MS/MS) data has become a major computational method for sequence identification in recent years. With the development of new instruments and technology, novel computational methods have emerged with enhanced performance. However, there are only a few methods focusing on ECD/ETD spectra, which mainly contain variants of -ions and -ions. Here, a de novo sequencing method for ECD/ETD spectra, NovoExD, is presented. NovoExD applies a new form of spectrum graph with multiple edge types (called a GMET), considers multiple peptide tags, and integrates amino acid combination (AAC) and fragment ion charge information. Its performance is compared with another successful de novo sequencing method, pNovo+, which has an option for ECD/ETD spectra. Experiments conducted on three different datasets show that the average full length peptide identification accuracy of NovoExD is as high as 88.70 percent, and that NovoExD's average accuracy is more than 20 percent greater on all datasets than that of pNovo+.]]>1423373441101<![CDATA[Identifying Spurious Interactions in the Protein-Protein Interaction Networks Using Local Similarity Preserving Embedding]]>142345352469<![CDATA[Microbiome Data Representation by Joint Nonnegative Matrix Factorization with Laplacian Regularization]]>142353359477<![CDATA[Predicting Protein Functions by Using Unbalanced Random Walk Algorithm on Three Biological Networks]]>142360369417<![CDATA[United Complex Centrality for Identification of Essential Proteins from PPI Networks]]>142370380757<![CDATA[A Flexible Computational Framework Using R and Map-Reduce for Permutation Tests of Massive Genetic Analysis of Complex Traits]]> up to or even more permutations can be needed. We have previously introduced the PruneDIRECT algorithm for multiple QTL scan with epistatic interactions. This algorithm has specific strengths for permutation testing. Here, we present a flexible, parallel computing framework for identifying multiple interacting QTL using the PruneDIRECT algorithm which uses the map-reduce model as implemented in Hadoop. The framework is implemented in R, a widely used software tool among geneticists. This enables users to rearrange algorithmic steps to adapt genetic models, search algorithms, and parallelization steps to their needs in a flexible way. Our work underlines the maturity of accessing distributed parallel computing for computationally demanding bioinformatics applications through building workflows within existing scientific environments. We investigate the PruneDIRECT algorithm, comparing its performance to exhaustive search and DIRECT algorithm using our framework on a public cloud resource. We find that PruneDIRECT is vastly superior for permutation testing, and perform permutations for a 2D QTL problem in hours, using cloud processes. We show that our framework scales out almost linearly for a 3D QTL search.]]>1423813921398<![CDATA[A New Scheme to Characterize and Identify Protein Ubiquitination Sites]]>142393403811<![CDATA[A Resolution of the Static Formulation Question for the Problem of Computing the History Bound]]>142404417893<![CDATA[Algorithms and Complexity Results for Genome Mapping Problems]]>142418430524<![CDATA[Extending and Applying Spartan to Perform Temporal Sensitivity Analyses for Predicting Changes in Influential Biological Pathways in Computational Models]]>spartan, an open-source sensitivity analysis toolkit designed to establish and understand the relationship between a computational model and the biological system that model captures. Here, we extend the functionality available in spartan to permit the production of statistical analyses that contrast the behavior exhibited by a computational model at various simulated time-points, enabling a temporal analysis that could suggest whether the influence of biological mechanisms changes over time. We exemplify this extended functionality by using the computational model of lymphoid tissue development as a time-lapse tool. By generating results at twelve- hour intervals, we show how the extensions to spartan have been used to suggest that lymphoid tissue development could be biphasic, and predict the time-point when a switch in the influence of biological mechanisms might occur.]]>142431442708<![CDATA[Genome-Wide Semi-Automated Annotation of Transporter Systems]]> merlin, a software tool previously developed by the authors, which expedites the GSMM reconstruction processes.]]>142443456500<![CDATA[Optimal Landmark Selection for Registration of 4D Confocal Image Stacks in Arabidopsis]]>1424574671126<![CDATA[A Characterization of Minimum Spanning Tree-Like Metric Spaces]]> points can be represented by a fully labeled tree on vertices, and thereby determine when an MST preserves all pairwise distances between points in a finite metric space.]]>142468471153<![CDATA[A Linear Bound on the Number of States in Optimal Convex Characters for Maximum Parsimony Distance]]>, the maximum parsimony distance is defined as the maximum, ranging over all characters on , of the absolute difference in parsimony score induced by on the two trees. In this note, we prove that for binary trees there exists a character achieving this maximum that is convex on one of the trees (i.e., the parsimony score induced on that tree is equal to the number of states in the character minus 1) and such that the number of states in the character is at most . This is the first non-trivial bound on the number of states required by optimal characters, convex or otherwise. The result potentially has algorithmic significance because, unlike general characters, convex characters with a bounded number of states can be enumerated in polynomial time.]]>142472477449<![CDATA[Building Ancestral Recombination Graphs for Whole Genomes]]>longest shared end for recombination inference, ARG4WG constructs ARGs with small numbers of recombination events that perform well in association mapping on genome-wide association studies.]]>142478483846<![CDATA[D-Map: Random Walking on Gene Network Inference Maps Towards differential Avenue Discovery]]>http://bioserver-3.bioacademy.gr/Bioserver/DMap/index.php.]]>142484490661<![CDATA[Metabolic Flux Analysis in Isotope Labeling Experiments Using the Adjoint Approach]]>Escherichia coli and are validated against reference software in the stationary case. The methods and algorithms described in this paper are included in the sysmetab software package distributed under an Open Source license at http://forge.scilab.org/index.php/p/sysmetab/.]]>142491497253<![CDATA[Pubcast and Genecast: Browsing and Exploring Publications and Associated Curated Content in Biology Through Mobile Devices]]>http://hive.biochemistry.gwu.edu/tools/HivePubcast.]]>142498500271