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IEEE/ACM Transactions on Computational Biology and Bioinformatics

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https://www.readbyqxmd.com/read/28541905/inferring-gene-species-assignments-in-the-presence-of-horizontal-gene-transfer
#1
Agnieszka Mykowiecka, Pawel Szczesny, Pawel Gorecki
BACKGROUND: Microbial communities from environmental samples show great diversity as bacteria quickly response to changes in their ecosystems. To assess the scenario of the actual changes, metagenomics experiments aimed at sequencing genomic DNA from such samples are performed. These new obtained sequences together with already known are used to infer phylogenetic trees assessing the taxonomic groups the species with these genes belong to. Here we propose the first approach to the gene-species assignment problem by using reconciliation with horizontal gene transfer...
May 24, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28541906/controlling-mrna-translation
#2
Yoram Zarai, Michael Margaliot, Eduardo Sontag, Tamir Tuller
The ribosomal density along different parts of the coding regions of the mRNA molecule affects various fundamental intracellular phenomena including: protein production rates, global ribosome allocation and organismal fitness, ribosomal drop off, co-translational protein folding, mRNA degradation, and more. Thus, regulating translation in order to obtain a desired ribosomal profile along the mRNA molecule is an important biological problem. We study this problem by using a dynamical model for mRNA translation, called the ribosome flow model (RFM)...
May 23, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28541223/efficient-algorithms-for-genomic-duplication-models
#3
Jaroslaw Paszek, Pawel Gorecki
An important issue in evolutionary molecular biology is to discover genomic duplication episodes and their correspondence to the species tree. Existing approaches vary in the two fundamental aspects: the choice of evolutionary scenarios that model allowed locations of duplications in the species tree, and the rules of clustering gene duplications from gene trees into a single multiple duplication event. Here we study the method of clustering called minimum episodes for several models of allowed evolutionary scenarios with a focus on interval models in which every gene duplication has an interval consisting of allowed locations in the species tree...
May 23, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28541224/protein-protein-interaction-interface-residue-pair-prediction-based-on-deep-learning-architecture
#4
Zhenni Zhao, Xinqi Gong
MOTIVATION: Proteins usually fulfill their biological functions by interacting with other proteins. Although some methods have been developed to predict the binding sites of a monomer protein, these are not sufficient for prediction of the interaction between two monomer proteins. The correct prediction of interface residue pairs from two monomer proteins is still an open question and has great significance for practical experimental applications in the life sciences. We hope to build a method for the prediction of interface residue pairs that is suitable for those applications...
May 19, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28541220/mgt-sm-a-method-for-constructing-cellular-signal-transduction-networks
#5
Min Li, Ruiqing Zheng, Yaohang Li, Fang-Xiang Wu, Jianxin Wang
A cellular signal transduction network is an important means to describe biological responses to environmental stimuli and exchange of biological signals. Constructing the cellular signal transduction network provides an important basis for the study of the biological activities, the mechanism of the diseases, drug targets and so on. The statistical approaches to network inference are popular in literature. Granger test has been used as an effective method for causality inference. Compared with bivariate granger tests, multivariate granger tests reduce the indirect causality and were used widely for the construction of cellular signal transduction networks...
May 19, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28541222/drug-target-prediction-by-multi-view-low-rank-embedding
#6
Limin Li, Menglan Cai
Drug repositioning has been a key problem in drug development, and heterogeneous data sources are used to predict drug-target interactions by different approaches. However, most of studies focus on a single representation of drugs or proteins. It has been shown that integrating multi-view representations of drugs and proteins can strengthen the prediction ability. For example, a drug can be represented by its chemical structure, or by its chemical response in different cells. A protein can be represented by its sequence, or by its gene expression values in different cells...
May 18, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28541221/subspace-weighting-co-clustering-of-gene-expression-data
#7
Xiaojun Chen, Joshua Zhexue Huang, Qingyao Wu, Min Yang
Microarray technology enables the collection of vast amounts of gene expression data from biological experiments. Clustering algorithms have been successfully applied to exploring the gene expression data. Since a set of genes may be possible correlated to a subset of samples, it is useful to use co-clustering to recover co-clusters in the gene expression data. In this paper, we propose a novel algorithm, called Subspace Weighting Co-Clustering (SWCC), for high dimensional gene expression data. In SWCC, a gene subspace weight matrix is introduced to identify the contribution of gene objects in distinguishing different sample clusters...
May 18, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28534784/incorporation-of-solvent-effect-into-multi-objective-evolutionary-algorithm-for-improved-protein-structure-prediction
#8
Shangce Gao, Jiujun Cheng, Yuki Todo, Mengchu Zhou
The problem of predicting the three-dimensional structure of a protein from its one-dimensional sequence has been called the "holy grail of molecular biology", and it has become an important part of structural genomics projects. Despite the rapid developments in computer technology and computational intelligence, it remains challenging and fascinating. In this paper, to solve it we propose a multi-objective evolutionary algorithm. We decompose the protein energy function Chemistry at HARvard Macromolecular Mechanics force fields into bond and non-bond energies as the first and second objectives...
May 17, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28534783/nahal-flex-a-numerical-and-alphabetical-hinge-detection-algorithm-for-flexible-protein-structure-alignment
#9
Samira Fotoohifiroozabadi, Mohd Saberi Mohamad, Safaai Deris
Flexible proteins are proteins that have conformational changes in their structures. Protein flexibility analysis is critical for classifying and understanding protein functionality. For that analysis, the hinge areas where proteins show flexibility must be detected. To detect the location of the hinges, previous methods have utilized the three-dimensional (3D) structure of proteins, which is highly computational. To reduce the computational complexity, this study proposes a novel text-based method using structural alphabets (SAs) for detecting the hinge position, called NAHAL-Flex...
May 17, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28534782/reinforce-an-ensemble-approach-for-inferring-ppi-network-from-ap-ms-data
#10
Bo Tian, Qiong Duan, Can Zhao, Ben Teng, Zengyou He
Affinity Purification-Mass Spectrometry (AP-MS) is one of the most important technologies for constructing protein-protein interaction (PPI) networks. In this paper, we propose an ensemble method, Reinforce, for inferring PPI network from AP-MS data set. The new algorithm named Reinforce is based on rank aggregation and false discovery rate control. Under the null hypothesis that the interaction scores from different scoring methods are randomly generated, Reinforce follows three steps to integrate multiple ranking results from different algorithms or different data sets...
May 17, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28534781/reviving-the-two-state-markov-chain-approach
#11
Andrzej Mizera, Jun Pang, Qixia Yuan
Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such systems. However, for large PBNs, which often arise in systems biology, obtaining the steady-state distribution poses a significant challenge. In this paper, we revive the two-state Markov chain approach to solve this problem. This paper contributes in three aspects. First, we identify a problem of generating biased results with the approach and we propose a few heuristics to avoid such a pitfall...
May 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28534780/katzlgo-large-scale-prediction-of-lncrna-functions-by-using-the-katz-measure-based-on-multiple-networks
#12
Zuping Zhang, Jingpu Zhang, Chao Fan, Yongjun Tang, Lei Deng
Aggregating evidences have shown that long non-coding RNAs (lncRNAs) generally play key roles in cellular biological processes such as epigenetic regulation, gene expression regulation at transcriptional and post-transcriptional levels, cell differentiation and others. However, most lncRNAs have not been functionally characterized. There is an urgent need to develop computational approaches for function annotation of increasing available lncRNAs. In this article, we propose a global network-based method, KATZLGO, to predict the functions of human lncRNAs at large scale...
May 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28504946/essential-protein-detection-by-random-walk-on-weighted-protein-protein-interaction-networks
#13
Bin Xu, Jihong Guan, Yang Wang, Zewei Wang
Essential proteins are critical to the development and survival of cells. Identification of essential proteins is helpful for understanding the minimal set of required genes in a living cell and for designing new drugs. To detect essential proteins, various computational methods have been proposed based on protein-protein interaction (PPI) networks. However, protein interaction data obtained by highthroughput experiments usually contain high false positives, which negatively impacts the accuracy of essential protein detection...
May 12, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28489545/learning-a-structural-and-functional-representation-for-gene-expressions-to-systematically-dissect-complex-cancer-phenotypes
#14
Yanbo Wang, Quan Liu, Shan Huang, Bo Yuan
Cancer is a heterogeneous disease, thus one of the central problems is how to dissect the resulting complex phenotypes in terms of their biological building blocks. Computationally, this is to represent and interpret high dimensional observations through a structural and conceptual abstraction into the most influential determinants underlying the problem. The working hypothesis of this report is to consider gene interaction to be largely responsible for the manifestation of complex cancer phenotypes, thus where the representation is to be conceptualized...
May 8, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28489544/an-improved-approach-for-n-linked-glycan-structure-identification-from-hcd-ms-ms-spectra
#15
Weiping Sun, Yi Liu, Gilles Lajoie, Bin Ma, Kaizhong Zhang
Glycosylation is a frequently observed posttranslational modification on proteins. Currently, tandem mass spectrometry (MS/MS) serves as an efficient analytical technique for characterizing structures of oligosaccharides. However, developing effective computational approaches for identifying glycan structures from mass spectra is still a great challenge in glycoproteomics research. In this study, we proposed an approach for matching the input spectra with glycan structures acquired from a glycan structure database by incorporating a de novo sequencing assisted ranking scheme...
May 5, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28489543/integrating-multiple-heterogeneous-networks-for-novel-lncrna-disease-association-inference
#16
Jingpu Zhang, Zuping Zhang, Zhigang Chen, Lei Deng
Accumulating experimental evidence has indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes implicated in many human diseases. However, only relatively few experimentally supported lncRNA-disease associations have been reported. Developing effective computational methods to infer lncRNA-disease associations is becoming increasingly important. Current network-based algorithms typically use a network representation to identify novel associations between lncRNAs and diseases...
May 4, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28489542/a-grouping-particle-swarm-optimizer-with-personal-best-position-guidance-for-large-scale-optimization
#17
Weian Guo, Chengyong Si, Yu Xue, Yanfen Mao, Lei Wang, Qidi Wu
Particle Swarm Optimization (PSO) is a popular algorithm which is widely investigated and well implemented in many areas. However, the canonical PSO does not perform well in population diversity maintenance so that usually leads to a premature convergence or local optima. To address this issue, we propose a variant of PSO named Grouping PSO with Personal- Best-Position (Pbest) Guidance (GPSO-PG) which maintains the population diversity by preserving the diversity of exemplars. On one hand, we adopt uniform random allocation strategy to assign particles into different groups and in each group the losers will learn from the winner...
May 4, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28463205/aggregation-for-computing-multi-modal-stationary-distributions-in-1-d-gene-regulatory-networks
#18
Neslihan Avcu, Nihal Pekergin, Ferhan Pekergin, Cuneyt Guzelis
This paper proposes aggregation-based, three-stage algorithms to overcome the numerical problems encountered in computing stationary distributions and mean first passage times for multi-modal birth-death processes of large state space sizes. The considered birth-death processes which are defined by Chemical Master Equations are used in modeling stochastic behavior of gene regulatory networks. Computing stationary probabilities for a multi-modal distribution from Chemical Master Equations is subject to have numerical problems due to the probability values running out of the representation range of the standard programming languages with the increasing size of the state space...
April 27, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28436887/optimal-block-based-trimming-for-next-generation-sequencing
#19
Ivo Hedtke, Ioana Lemnian, Ivo Grosse, Matthias Muller-Hannemann
Read trimming is a fundamental first step of the analysis of next generation sequencing (NGS) data. Traditionally, it is performed heuristically, and algorithmic work in this area has been neglected. Here, we address this topic and formulate three optimization problems for block-based trimming (truncating the same low-quality positions at both ends for all reads and removing low-quality truncated reads). We find that all problems are NP-hard. Hence, we investigate the approximability of the problems. Two of them are NP-hard to approximate...
April 24, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28436886/dna-assembly-with-de-bruijn-graphs-using-an-fpga-platform
#20
Carl Poirier, Benoit Gosselin, Paul Fortier
This paper presents an FPGA implementation of a DNA assembly algorithm, called Ray, initially developed to run on parallel CPUs. The OpenCL language is used and the focus is placed on modifying and optimizing the original algorithm to better suit the new parallelization tool and the radically different hardware architecture. The results show that the execution time is roughly one fourth that of the CPU and factoring energy consumption yields a tenfold savings.
April 24, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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