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IEEE/ACM Transactions on Computational Biology and Bioinformatics

Yingxia Sun, Xuan Wang, Junliang Shang, Jin-Xing Liu, Chun-Hou Zheng, Xiujuan Lei
Epistasis learning, which is aimed at detecting associations between multiple Single Nucleotide Polymorphisms (SNPs) and complex diseases, has gained increasing attention in genome wide association studies. Although much work has been done on mapping the SNPs underlying complex diseases, there is still difficulty in detecting epistatic interactions due to the lack of heuristic information to expedite the search process. In this study, a method EACO is proposed to detect epistatic interactions based on ant colony optimization (ACO) algorithm, the highlights of which are the introduced heuristic information, fitness function, and a candidate solutions filtration strategy...
November 5, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Nataliya Sokolovska, Olga Permiakova, Kristoffer Forslund, Jean-Daniel Zucker
An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem of causal inference in a bivariate case, where the joint distribution of two variables is observed. In this contribution, we introduce a novel method of causality discovering which is based on the widely used assumption that if X causes Y, then P(X) and P(Y|X) are independent...
November 5, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Huiru Zheng, Haiying Wang, Richard Dewhurst, Rainer Roehe
The importance of the composition and signature of rumen microbial communities has gained increasing attention. One of the key techniques was to infer co-abundance networks through correlation analysis based on relative abundances. In this study, we proposed the use of a framework including a compendium of two correlation measures and three dissimilarity metrics to mitigate the compositional effect in the inference of significant associations in the bovine rumen microbiome. We tested the framework on rumen microbiome data including both 16S rRNA and KEGG genes associated with methane production in cattle...
November 2, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Meirav Zehavi, Dor Ganor, Ron Pinter
Discrete simulations of genetic regulatory networks were used to study subsystems of yeast successfully. However, implementations of existing models underlying these simulations do not support a graphic interface, and require computations necessary to analyze their results to be done manually. Furthermore, differences between existing models suggest that an enriched model, encompassing both existing models, is needed. We developed a software tool, GRegNetSim, that allows the end-user to describe genetic regulatory networks graphically...
October 30, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Zhi-Zhong Chen, Youta Harada, Yuna Nakamura, Lusheng Wang
Due to hybridization events in evolution, studying two different genes of a set of species may yield two related but different phylogenetic trees for the set of species. In this case, we want to measure the dissimilarity of the two trees. The rooted subtree prune and regraft (rSPR) distance of the two trees has been used for this purpose. The problem of computing the rSPR distance of two given trees has many applications but is NP-hard. Accordingly, a number of programs have been developed for solving the problem either exactly or approximately...
October 30, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiaoyu Zhang, Xiangke Liao, Hao Zhu, Kenli Li, Benyun Shi, Shaoliang Peng
Co-evolution exists ubiquitously in biological systems. At the molecular level, interacting proteins, such as ligands and their receptors and components in protein complexes, co-evolve to maintain their structural and functional interactions. Many proteins contain multiple functional domains interacting with different partners, making co-evolution of interacting domains occur more prominently. Multiple methods have been developed to predict interacting proteins or domains within proteins by detecting their co-variation...
October 30, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Qiwen Kang, Christopher W Schardl, Neil Moore, Ruriko Yoshida
Evolutionary hypotheses provide important underpinnings of biological and medical sciences, and comprehensive, genome-wide understanding of evolutionary relationships among organisms are needed to test and refine such hypotheses. Theory and empirical evidence clearly indicate that phylogenies (trees) of different genes (loci) should not display precisely matching topologies. The main reason for such phylogenetic incongruence is reticulated evolutionary history of most species due to meiotic sexual recombination in eukaryotes, or horizontal transfers of genetic material in prokaryotes...
October 30, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Mustafa Alshawaqfeh, Ahmad Al Kawam, Erchin Serpedin, Aniruddha Datta
The study of recurrent copy number variations (CNVs) plays an important role in understanding the onset and evolution of complex diseases such as cancer. Array-based comparative genomic hybridization (aCGH) is a widely used microarray based technology for identifying CNVs. However, due to high noise levels and inter-sample variability, detecting recurrent CNVs from aCGH data remains a challenging topic. This paper proposes a novel method for identification of the recurrent CNVs. In the proposed method, the noisy aCGH data is modeled as the superposition of three matrices: a full-rank matrix of weighted piece-wise generating signals accounting for the clean aCGH data, a Gaussian noise matrix to model the inherent experimentation errors and other sources of error, and a sparse matrix to capture the sparse inter-sample (sample-specific) variations...
October 30, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Abir Hussain, Dhiya Al-Jumeily, Ahmed Aljaaf, Naeem Radi
Diabetes is one of the main public health chronic conditions that are potentially reaching epidemic proportions globally. Worldwide, the occurrence of these types of diseases are increasing sharply at a worrying degree, with death of around 18 million people every year from cardiovascular disease, for which diabetes and hypertension are major predisposing factors. Two major concerns are that much of this increase in Diabetes is predicated to be happened in developing countries, with a growing incidence of Type 2 Diabetes (T2D) at a younger age including some obese children even before puberty...
October 30, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Firat Ismailoglu, Rachel Cavill, Evgueni Smirnov, Shuang Zhou, Pieter Collins, Ralf Peeters
Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e...
October 24, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Binbin Wu, Min Li, Xingyu Liao, Junwei Luo, Fangxiang Wu, Yi Pan, Jianxin Wang
The de novo assembly tools aim at reconstructing genomes from next-generation sequencing (NGS) data. However, the assembly tools usually generate a large amount of contigs containing many misassemblies, which are caused by problems of repetitive regions, chimeric reads and sequencing errors. As they can improve the accuracy of assembly results, detecting and correcting the misassemblies in contigs are appealing, yet challenging. In this study, a novel method, called MEC, is proposed to identify and correct misassemblies in contigs...
October 18, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiguo Yuan, Meihong Gao, Jun Bai, Junbo Duan
Structural variation accounts for a major fraction of mutations in the human genome and confers susceptibility to complex diseases. Next generation sequencing along with the rapid development of computational methods provides a cost-effective procedure to detect such variations. Simulation of structural variations and sequencing reads with real characteristics is essential for benchmarking the computational methods. Here, we develop a new program, SVSR, to simulate five types of structural variations (indels, tandem duplication, CNVs, inversions, and translocations) and SNPs for the human genome and to generate sequencing reads with features from popular platforms (Illumina, SOLiD, 454, and Ion Torrent)...
October 17, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Somayeh Bakhteh, Alireza Ghaffari-Hadigheh, Nader Chaparzadeh
Identification of master regulatory genes is one of the primary challenges in systems biology. The minimum dominating set problem is a powerful paradigm in analyzing such complex networks. In these models, genes stand as nodes and their interactions are assumed as edges. Here, members of a minimal dominating set could be regarded as master genes. As finitely many minimum dominating sets may exist in a network, it is difficult to identify which one represents the most appropriate set of master genes. In this paper, we develop a weighted gene regulatory network problem with two objectives as a version of the dominating set problem...
October 12, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Fatima Boukari, Sokratis Makrogiannis
Automated cell segmentation and tracking enables the quantification of static and dynamic cell characteristics and is significant for disease diagnosis, treatment, drug development and other biomedical applications. This paper introduces a method for fully automated cell tracking, lineage construction, and quantification. Cell detection is performed in the joint spatio-temporal domain by a motion diffusion-based Partial Differential Equation (PDE) combined with energy minimizing active contours. In the tracking stage, we adopt a variational joint local-global optical flow technique to determine the motion vector field...
October 12, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Shailima Rampogu, Ayoung Baek, Rohit S Bavi, Minky Son, Guang Ping Cao, Raj Kumar, Chanin Park, Amir Zeb, Rabia Mukthar Rana, Seok Ju Park, Keun Woo Lee
Aromatase inhibitors with an IC50 value ranging from 1.4 to 49.7uM are known to act as antiepileptic drugs besides being potential breast cancer inhibitors. The aim of the present study is to identify novel antiepileptic aromatase inhibitors with higher activity exploiting the ligand-based pharmacophore approach utilizing the experimentally known inhibitors. The resultant Hypo1 consists of four features and was further validated by using three different strategies. Hypo1 was allowed to screen different databases to identify lead molecules and were further subjected to Lipinski' Rule of Five and ADMET to establish their drug-like properties...
October 12, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Arash Bayat, Nandan P Deshpande, Marc R Wilkins, Sri Parameswaran
The de-novo genome assembly is a challenging computational problem for which several pipelines have been developed. The advent of long-read sequencing technology has resulted in a new set of algorithmic approaches for the assembly process. In this work, we identify that one of these new and fast long-read assembly techniques (using Minimap2 and Miniasm) can be modified for the short-read assembly process. This possibility motivated us to customize a long-read assembly approach for applications in a short-read assembly scenario...
October 11, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Lei Wang, Zhu-Hong You, De-Shuang Huang, Fengfeng Zhou
Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological experiments provide a lot of valuable information for the initial identification of RNA-protein interactions (RPIs), but with the increasing complexity of RPIs networks, this method gradually falls into expensive and time-consuming situations. Therefore, there is an urgent need for high speed and reliable methods to predict RNA-protein interactions...
October 5, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Su-Ping Deng, Wei-Li Guo
Liver cancer is one of the deadliest cancers in the world. To find effective therapies for this cancer, it is indispensable to identify key genes, which may play critical roles in the incidence of the liver cancer. To identify key genes of the liver cancer with high accuracy, we integrated multiple microarray gene expression data sets to compute common differentially expressed genes, which will result more accurate than those from individual data set. To find the main functions or pathways that these genes are involved in, some enrichment analyses were performed including functional enrichment analysis, pathway enrichment analysis, and disease association study...
October 5, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Hongyu Liu, Qinmin Hu, Liang He
Feature learning and selection have been widely applied in many research areas because of their good performance and lower complexity. Traditional methods usually treat all terms with same feature sets, such that performance can be damaged when noisy information is brought by wrong features for a given term. In this paper, we propose a term-based personalization approach to finding the best features for each term. First, features are given as the input so that we focus on selection strategies. Second, we present a feature searching method to generate feature candidate subsets for each term...
October 5, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Guijun Zhang, Laifa Ma, Xiaoqi Wang, Xiaogen Zhou
Ab initio protein tertiary structure prediction is one of the long-standing problems in structural bioinformatics. With the help of the residue-residue contact and secondary structure prediction information, the accuracy of ab initio structure prediction can be enhanced. In this study, an improved differential evolution using the secondary structure and residue-residue contact information referred to as SCDE is proposed for protein structure prediction. In SCDE, two score models based on the secondary structure and contact information are proposed, and two selection strategies, namely, secondary structure-based selection strategy and contact-based selection strategy, are designed to guide conformation space search...
October 4, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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