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IEEE/ACM Transactions on Computational Biology and Bioinformatics

Nivit Grewal, Shailendra Singh, Trilok Chand
Owing to the innate noise in the biological data sources, a single source or a single measure do not suffice for an effective disease gene prioritization. So, the integration of multiple data sources or aggregation of multiple measures is the need of the hour. The aggregation operators combine multiple related data values to a single value such that the combined value has the effect of all the individual values. In this paper, an attempt has been made for applying the fuzzy aggregation on the network-based disease gene prioritization and investigate its effect under noise conditions...
November 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Alireza Karbalayghareh, Ulisses Braga-Neto, Edward Russell Dougherty
This paper studies classification of gene-expression trajectories coming from two classes, healthy and mutated (cancerous) using Boolean networks with perturbation (BNps) to model the dynamics of each class at the state level. Each class has its own BNp, which is partially known based on gene pathways. We employ a Gaussian model at the observation level to show the expression values of the genes given the hidden binary states at each time point. We use expectation maximization (EM) to learn the BNps and the unknown model parameters, derive closed-form updates for the parameters, and propose a learning algorithm...
October 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Kavitha Mukund, Samuel R Ward, Richard L Lieber, Shankar Subramaniam
Botulinum Neurotoxin A (BoNT-A) is a potent neurotoxin with several clinical applications.The goal of this study was to utilize co-expression network theory to analyze temporal transcriptional data from skeletal muscle after BoNT-A treatment. Expression data for 2000 genes (extracted using a ranking heuristic) served as the basis for this analysis. Using weighted gene co-expression network analysis (WGCNA), we identified 19 co-expressed modules, further hierarchically clustered into 5 groups. Quantifying average expression and co-expression patterns across these groups revealed temporal aspects of muscle's response to BoNT-A...
October 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Alexey Markin, Oliver Eulenstein
Synthesizing large-scale phylogenetic trees is a fundamental problem in evolutionary biology. Median tree problems have evolved as a powerful tool to reconstruct such trees. Such problems seek a median tree for a given collection of input trees under some problem-specific tree distance. There has been an increased interest in the median tree problem for the classical path-difference distance between trees. While this problem is NP-hard, standard local search heuristics have been described that are based on solving a local search problem exactly...
October 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Youyuan Li, Yingping Zhuang
Peptide mass fingerprinting continues to play an important role in current proteomics studies based on its good performance in sample throughput, specificity for single peptides, and insensitive to unexpected post-translational modifications as compared with . Here, we proposed and evaluated the use of feature-matching pattern-based support vector machines (SVMs) for robust protein identification. This approach is now facilitated with an updated web server (fmpRPMF) incorporated with several newly developed or improved modules and workflows allowing identification of proteins from data...
October 13, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Kin On Cheng, Ngai Fong Law, W-C Siu
Due to the advancement of DNA sequencing techniques, the number of sequenced individual genomes has experienced an exponential growth. Thus, effective compression of this kind of sequences is highly desired. In this work, we present a novel compression algorithm called Reference-based Compression algorithm using the concept of Clustering (RCC). The rationale behind RCC is based on the observation about the existence of substructures within the population sequences. To utilize these substructures, k-means clustering is employed to partition sequences into clusters for better compression...
October 12, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Juntao Li, Wenpeng Dong, Deyuan Meng
This paper deals with the problems of cancer classification and grouped gene selection. The weighted gene co-expression network on cancer microarray data is employed to identify modules corresponding to biological pathways, based on which a strategy of dividing genes into groups is presented. Using the conditional mutual information within each divided group, an integrated criterion is proposed and the data-driven weights are constructed. They are shown with the ability to evaluate both the individual gene significance and the influence to improve correlation of all the other pairwise genes in each group...
October 11, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jorge Gonzalez-Dominguez, Maria J Martin
In this work we present MPIGeneNet, a parallel tool that applies Pearson's correlation and Random Matrix Theory to construct gene co-expression networks. It is based on the state-of-the-art sequential tool RMTGeneNet, which provides networks with high robustness and sensitivity at the expenses of relatively long runtimes for large scale input datasets. MPIGeneNet returns the same results as RMTGeneNet but improves the memory management, reduces the I/O cost, and accelerates the two most computationally demanding steps of co-expression network construction by exploiting the compute capabilities of common multicore CPU clusters...
October 10, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiaoke Ma, Penggang Sun, Guimin Qin
Condition-specific modules in multiple networks must be determined to reveal the underlying molecular mechanisms of diseases. Current algorithms exhibit limitations such as low accuracy and high sensitivity to the number of networks because these algorithms discover condition-specific modules in multiple networks by separating specificity and modularity of modules. To overcome these limitations, we characterize condition-specific module as a group of genes whose connectivity is strong in the corresponding network and weak in other networks; this strategy can accurately depict the topological structure of condition-specific modules...
October 10, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Enrico Siragusa, Niina Haiminen, Filippo Utro, Laxmi Parida
Computer simulations can be used to study population genetic methods, models and parameters, as well as to predict potential outcomes. For example, in plant populations, predicting the outcome of breeding operations can be studied using simulations. In-silico construction of populations with pre-specified characteristics is an important task in breeding optimization and other population genetic studies. We present two linear time Simulation using Best-fit Algorithms (SimBA) for two classes of problems where each co-fits two distributions: SimBA-LD fits linkage disequilibrium and minimum allele frequency distributions, while SimBA-hap fits founder-haplotype and polyploid allele dosage distributions...
October 9, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Hongjie Wu, Chengyuan Cao, Xiaoyan Xia, Qiang Lu
Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences...
October 9, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Tony Pan, Patrick Flick, Chirag Jain, Yongchao Liu, Srinivas Aluru
Counting and indexing fixed length substrings, or k-mers, in biological sequences is a key step in many bioinformatics tasks including genome alignment and mapping, genome assembly, and error correction. While advances in next generation sequencing technologies have dramatically reduced the cost and improved latency and throughput, few bioinformatics tools can efficiently process the datasets at the current generation rate of 1.8 terabases every 3 days. We present Kmerind, a high performance parallel k-mer indexing library for distributed memory environments...
October 9, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Ho-Chun Wu, Xi-Guang Wei, Shing-Chow Chan
This paper proposes novel consensus gene selection criteria for partial least squares-based gene microarray analysis. By quantifying the extent of consistency and distinctiveness of the differential gene expressions across different double cross validations (CV) or randomizations in terms of occurrence and randomization p-values, the proposed criteria are able to identify a more comprehensive genes associated with the underlying disease. A Distributed GPU implementation has been proposed to accelerate the gene selection problem and about 8-11 times speed up has been achieved based on the microarray datasets considered...
October 9, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Min Liu, Yue He, Weili Qian, Yangliu Wei, Xiaoyan Liu
Developing algorithms for plant cell population tracking is very critical for the modeling of plant cell growth pattern and gene expression dynamics. The tracking of plant cells in microscopic image stacks is very challenging for several reasons: (1) plant cells are densely packed in a specific honeycomb structure; (2) they are frequently dividing; (3) they are imaged in different layers within 3D image stacks. Based on an existing 2D local graph matching algorithm, this paper focuses on building a 3D plant cell matching model, by exploiting the cells' 3D spatiotemporal context...
October 6, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Alejandro F Villaverde, Kolja Becker, Julio R Banga
Inferring the structure of unknown cellular networks is a main challenge in computational biology. Data-driven approaches based on information theory can determine the existence of interactions among network nodes automatically. However, the elucidation of certain features - such as distinguishing between direct and indirect interactions or determining the direction of a causal link - requires estimating information-theoretic quantities in a multidimensional space. This can be a computationally demanding task, which acts as a bottleneck for the application of elaborate algorithms to large-scale network inference problems...
October 4, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Mohammad Arifur Rahman, Nathan LaPierre, Huzefa Rangwala
The recent advent of Metagenome Wide Association Studies (MGWAS) provides insight into the role of microbes on human health and disease. However, the studies present several computational challenges. In this paper we demonstrate a novel, efficient, and effective Multiple Instance Learning (MIL) based computational pipeline to predict patient phenotype from metagenomic data. MIL methods have the advantage that besides predicting the clinical phenotype, we can infer the instance level label or role of microbial sequence reads in the specific disease...
October 4, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Renjie Tan, Jixuan Wang, Xiaoliang Wu, Liran Juan, Likun Zheng, Rui Ma, Qing Zhan, Tao Wang, Shuilin Jin, Qinghua Jiang, Yadong Wang
Copy number variants (CNVs) play important roles in human disease and evolution. With the rapid development of next-generation sequencing technologies, many tools have been developed for inferring CNVs based on whole-exome sequencing (WES) data. However, as a result of the sparse distribution of exons in the genome, the limitations of the WES technique, and the nature of high-level signal noises in WES data, the efficacy of these variants remains less than desirable. Thus, there is need for the development of an effective tool to achieve a considerable power in WES CNVs discovery...
October 4, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Shubhanshu Shekhar, Sebastien Roch, Siavash Mirarab
Species tree reconstruction from genomic data is increasingly performed using methods that account for sources of gene tree discordance such as incomplete lineage sorting. One popular method for reconstructing species trees from unrooted gene tree topologies is ASTRAL. In this paper, we derive theoretical sample complexity results for the number of genes required by ASTRAL to guarantee reconstruction of the correct species tree with high probability. We also validate those theoretical bounds in a simulation study...
September 29, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Wenzheng Bao, Chuan-An Yuan, Younhua Zhang, Kyungsook Han, Asoke K Nandi, Barry Honig, De-Shuang Huang
The post translational modification plays a significiant role in the biological processing. The potential post translational modification is composed of the center sites and the adjacent amino acid residues which are fundamental protein sequence residues.It can be helpful to perform their biological functions and contribute to understanding the molecular mechanisms that are the foundations of protein design and drug design. The existing algorithms of predicting modified sites often have some shortcomings, such as lower stability and accuracy...
September 28, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Mohammad Nazrul Ishlam Patoary, Carl Tropper, Robert A McDougal, Lin Zhongwei, William W Lytton
The intra-cellular calcium signaling pathways of a neuron depends on both biochemical reactions and diffusions. Some quasi-isolated compartments (e.g. spines) are so small and calcium concentrations are so low that one extra molecule diffusing in by chance can make a nontrivial difference in its concentration (percentage-wise). These rare events can affect dynamics discretely in such way that they cannot be evaluated by a deterministic simulation. Stochastic models of such a system provide a more detailed understanding of these systems than existing deterministic models because they capture their behavior at a molecular level...
September 26, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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