<![CDATA[ IEEE/ACM Transactions on Computational Biology and Bioinformatics - new TOC ]]>
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TOC Alert for Publication# 8857 2018April 19<![CDATA[Selected Papers of the First International Conference on Algorithms for Computational Biology (AlCoB 2014)]]>IEEE/ACM Transactions on Computational Biology and Bioinformatics contains extended versions of the best papers presented at the First International Conference on Algorithms for Computational Biology (AlCoB 2014). Out of 39 submissions to the conference, only four papers representing the current state-of-the-art in their respective domains were accepted to this special section.]]>15235035181<![CDATA[A GRASP-Based Heuristic for the Sorting by Length-Weighted Inversions Problem]]>Yersinia genus and we compared our trees to other results in the literature.]]>1523523631070<![CDATA[Optimal Block-Based Trimming for Next Generation Sequencing]]>optimized block trimming algorithms to 12 data sets from three species, four sequencers, and read lengths ranging from 36 to 101 bp and find that (i) the omitted constraints are indeed almost always satisfied, (ii) the optimized read trimming algorithms typically yield a higher number of untrimmed bases than traditional heuristics, and (iii) these results can be generalized to alternative objective functions beyond counting the number of untrimmed bases.]]>152364376754<![CDATA[Modeling the Geometry and Dynamics of the Endoplasmic Reticulum Network]]>152377386981<![CDATA[SparseNCA: Sparse Network Component Analysis for Recovering Transcription Factor Activities with Incomplete Prior Information]]>$ell _1$ norm, which results in a greater estimation accuracy. In order to improve the computational efficiency, an iterative re-weighted $ell _2$ method is proposed for the NCA problem which not only promotes sparsity but is hundreds of times faster than the $ell _1$ norm based solution. The performance of sparseNCA is rigorously compared to that of FastNCA and NINCA using synthetic data as well as real data. It is shown that sparseNCA outperforms the existing state-of-the-art algorithms both in terms of estimation accuracy and consistency with the added advantage of low computational complexity. The performance of sparseNCA compared to its predecessors is particularly pronounced in case of incomplete prior info-
mation about the sparsity of the network. Subnetwork analysis is performed on the E.coli data which reiterates the superior consistency of the proposed algorithm.]]>152387395527<![CDATA[Guest Editorial for the 14<sup>th</sup> Asia Pacific Bioinformatics Conference]]>152396397141<![CDATA[Autumn Algorithm—Computation of Hybridization Networks for Realistic Phylogenetic Trees]]>1523984102223<![CDATA[DTL-RnB: Algorithms and Tools for Summarizing the Space of DTL Reconciliations]]>$1-frac{1}{e}$ approximation algorithm, experimental results that indicate its effectiveness, and the new DTL-RnB software tool that uses our algorithms to summarize the space of optimal reconciliations (www.cs.hmc.edu/dtlrnb).]]>152411421649<![CDATA[Algorithms for Pedigree Comparison]]>$O(n(1+sqrt{2})^k)$, where $n$ is the number of vertices in the two input pedigrees and $k$ is the number of edges to be cut. This algorithm is useful in practice when comparing two similar pedigrees.]]>152422431734<![CDATA[Predicting the Absorption Potential of Chemical Compounds Through a Deep Learning Approach]]>152432440699<![CDATA[hc-OTU: A Fast and Accurate Method for Clustering Operational Taxonomic Units Based on Homopolymer Compaction]]>1524414511164<![CDATA[Codon Context Optimization in Synthetic Gene Design]]>152452459565<![CDATA[3D Genome Reconstruction with ShRec3D+ and Hi-C Data]]>152460468875<![CDATA[Algorithmic Mapping and Characterization of the Drug-Induced Phenotypic-Response Space of Parasites Causing Schistosomiasis]]>S. mansoni – one of the etiological agents of schistosomiasis, induced by compounds that target its polo-like kinase 1 (PLK 1) gene – a recently validated drug target. In our approach, first, bio-image analysis algorithms are used to quantify the phenotypic responses of d-
fferent drugs. Next, these responses are linearly mapped into a low- dimensional space using Principle Component Analysis (PCA). The phenotype space is modeled using neighborhood graphs which are used to represent the similarity amongst the phenotypes. These graphs are characterized and explored using network analysis algorithms. We present a number of results related to both the nature of the phenotypic space of the S. mansoni parasite as well as algorithmic issues encountered in constructing and analyzing the phenotypic-response space. In particular, the phenotype distribution of the parasite was found to have a distinct shape and topology. We have also quantitatively characterized the phenotypic space by varying critical model parameters. Finally, these maps of the phenotype space allows visualization and reasoning about complex relationships between putative drugs and their system-wide effects and can serve as a highly efficient paradigm for assimilating and unifying information from phenotypic screens both during lead identification and lead optimization.]]>152469481861<![CDATA[Deep Sequencing Data Analysis]]>152482483135<![CDATA[Detecting Multivariate Gene Interactions in RNA-Seq Data Using Optimal Bayesian Classification]]> http://bit.ly/obc_package.]]>152484493943<![CDATA[Examining De Novo Transcriptome Assemblies via a Quality Assessment Pipeline]]>de novo transcriptome assembly and annotation methods provide an incredible opportunity to study the transcriptome of organisms that lack an assembled and annotated genome. There are currently a number of de novo transcriptome assembly methods, but it has been difficult to evaluate the quality of these assemblies. In order to assess the quality of the transcriptome assemblies, we composed a workflow of multiple quality check measurements that in combination provide a clear evaluation of the assembly performance. We presented novel transcriptome assemblies and functional annotations for Pacific Whiteleg Shrimp (Litopenaeus vannamei ), a mariculture species with great national and international interest, and no solid transcriptome/genome reference. We examined Pacific Whiteleg transcriptome assemblies via multiple metrics, and provide an improved gene annotation. Our investigations show that assessing the quality of an assembly purely based on the assembler's statistical measurements can be misleading; we propose a hybrid approach that consists of statistical quality checks and further biological-based evaluations.]]>152494505875<![CDATA[Computational Prediction of Pathogenic Network Modules in Fusarium verticillioides]]>Fusarium verticillioides is a fungal pathogen that triggers stalk rots and ear rots in maize. In this study, we performed a comparative analysis of wild type and loss-of-virulence mutant F. verticillioides co-expression networks to identify subnetwork modules that are associated with its pathogenicity. We constructed the F. verticillioides co-expression networks from RNA-Seq data and searched through these networks to identify subnetwork modules that are differentially activated between the wild type and mutant F. verticillioides, which considerably differ in terms of pathogenic potentials. A greedy seed-and-extend approach was utilized in our search, where we also used an efficient branch-out technique for reliable prediction of functional subnetwork modules in the fungus. Through our analysis, we identified four potential pathogenicity-associated subnetwork modules, each of which consists of interacting genes with coordinated expression patterns, but whose activation level is significantly different in the wild type and the mutant. The predicted modules were comprised of functionally coherent genes and topologically cohesive. Furthermore, they contained several orthologs of known pathogenic genes in other fungi, which may play important roles in the fungal pathogenesis.]]>152506515696<![CDATA[Optimal Fault Detection and Diagnosis in Transcriptional Circuits Using Next-Generation Sequencing]]>p53-MDM2 negative feedback loop Boolean network with stuck-at faults that model molecular events commonly found in cancer.]]>152516525672<![CDATA[MeTDiff: A Novel Differential RNA Methylation Analysis for MeRIP-Seq Data]]>6A) transcriptome methylation is an exciting new research area that just captures the attention of research community. We present in this paper, MeTDiff, a novel computational tool for predicting differential m^{6}A methylation sites from Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data. Compared with the existing algorithm exomePeak, the advantages of MeTDiff are that it explicitly models the reads variation in data and also devices a more power likelihood ratio test for differential methylation site prediction. Comprehensive evaluation of MeTDiff's performance using both simulated and real datasets showed that MeTDiff is much more robust and achieved much higher sensitivity and specificity over exomePeak. The R package “MeTDiff” and additional details are available at: https://github.com/compgenomics/MeTDiff.]]>152526534737<![CDATA[Probabilistic Methods in Computational Neuroscience]]>15253553699<![CDATA[Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data]]>152537550792<![CDATA[Gender Identification of Human Brain Image with A Novel 3D Descriptor]]>1525515611146<![CDATA[A Copula-Based Granger Causality Measure for the Analysis of Neural Spike Train Data]]>152562569655<![CDATA[Cortical Thinning and Cognitive Impairment in Parkinson's Disease without Dementia]]>152570580949<![CDATA[Complex Network Measures in Autism Spectrum Disorders]]>152581587549<![CDATA[A New Efficient Algorithm for the Frequent Gene Team Problem]]>m genomes, the problem is to find gene teams that occur in at least μ of the given genomes. In this paper, a new algorithm is presented. Previous solutions are efficient only when μ is small. Unlike previous solutions, the presented algorithm does not rely on examining every combination of μ genomes. Its time complexity is independent of μ. Under some realistic assumptions, the practical running time is estimated to be $O(m^{2}n^{2}; {mathrm{lg}};n)$, where n is the maximum length of the input genomes. Experiments showed that the presented algorithm is extremely efficient. For any μ, it takes less than 1 second to process 100 bacterial genomes and takes only 10 minutes to process 2,000 genomes. The presented algorithm can be used as an effective tool for large scale genome analyses.]]>152588598523<![CDATA[A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data]]>152599612294<![CDATA[Application of Fractal Theory on Motifs Counting in Biological Networks]]>1526136231265<![CDATA[Classification of Alzheimer's Disease Using Whole Brain Hierarchical Network]]>$F-$ scores. Finally, we use multiple kernel boosting (MKBoost) algorithm to perform the classification. Our proposed method is evaluated on MRI images of 710 subjects (200 AD, 120 MCIc, 160 MCInc, and 230 HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed method achieves an accuracy of 94.65 percent and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.954 for AD/HC classification, an accuracy of 89.63 percent and an AUC of 0.907 for AD/MCI classification, an-
accuracy of 85.79 percent and an AUC of 0.826 for MCI/HC classification, and an accuracy of 72.08 percent and an AUC of 0.716 for MCIc/MCInc classification, respectively. Our results demonstrate that our proposed method is efficient and promising for clinical applications for the diagnosis of AD via MRI images.]]>152624632958<![CDATA[Enumerating Substituted Benzene Isomers of Tree-Like Chemical Graphs]]>$G$ with no other cycles than benzene rings is called tree-like, and becomes a tree $T$ possibly with multiple edges if we contract each benzene ring into a single virtual atom of valence 6. All tree-like chemical graphs with a given tree representation $T$ are called the substituted benzene isomers of $T$. When we replace each virtual atom in $T$ with a benzene ring to obtain a substituted benzene isomer, distinct isomers of $T$ are caused by the difference in arrangements of atom groups around a benzene ring. In this paper, we propose an efficient algorithm that enumerates all substituted benzene isomers of a given tree representation $T$ . Our algorithm first counts the number $f$ of all the isomers of the tree representation by a dynamic programming method. To enumerate all the isomers, for each $k=1,2,ldots, f$, our algorithm then generates the $k$th isomer by backtracking the counting phase of the dynamic programming. We also implemented our algorithm for computational experiments.]]>152633646719<![CDATA[Extracting Stage-Specific and Dynamic Modules Through Analyzing Multiple Networks Associated with Cancer Progression]]>NMF-DM), which simultaneously analyzes multiple networks for the identification of stage-specific and dynamic modules. NMF-DM applies the temporal smoothness framework by balancing the networks at the current stage and the previous stage. Experimental results indicate that the NMF-DM algorithm is more accurate than the state-of-the-art methods in artificial dynamic networks. In breast cancer networks, NMF-DM reveals the dynamic modules that are important for cancer stage transitions. Furthermore, the stage-specific and dynamic modules have distinct topological and biochemical properties. Finally, we demonstrate that the stage-specific modules significantly improve the accuracy of cancer stage prediction. The proposed algorithm provides an effective way to explore the time-dependent cancer genomic data.]]>1526476581311<![CDATA[Fuzzy-Rough Entropy Measure and Histogram Based Patient Selection for miRNA Ranking in Cancer]]>$F$ score. While for these data sets the $F$ score of the miRNAs selected by our method varies from 0.70 to 0.91 using SVM, those results vary from 0.37 to 0.90 for some other methods. Moreover, all the selected miRNAs corroborate with the findings of biological investigations or pathway analysis tools. The source code of FREM is available at http://www.jayanta.droppages.com/FREM.html.]]>1526596721193<![CDATA[Integrating Multiple Data Sources for Combinatorial Marker Discovery: A Study in Tumorigenesis]]>$SCM$s) using both gene expression and methylation data. The gene expression and methylation data are integrated into a single continuous data as well as a (post-discretized) boolean data based on their intrinsic (i.e., inverse) relationship. A novel combined score of methylation and expression data (viz., $CoMEx$) is introduced which is computed on the integrated continuous data for identifying initial non-redundant set of genes. Thereafter, (maximal) frequent closed homogeneous genesets are identified using a well-known biclustering algorithm applied on the integrated boolean data of the determined non-redundant set of genes. A novel sample-based weighted support ( $WS$) is then proposed that is consecutively calculated on the integrated boolean data of the determined non-redundant set of genes in order to identify the non-redundant significant genesets. The top few resulting genesets are identified as potential $SCM$s. Since our proposed method generates a smaller number of significant non-redundant genesets than those by other popular methods, the method is much faster than the others. Application of the proposed techniq-
e on an expression and a methylation data for Uterine tumor or Prostate Carcinoma produces a set of significant combination of markers. We expect that such a combination of markers will produce lower false positives than individual markers.]]>1526736871806