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IEEE/ACM Transactions on Computational Biology and Bioinformatics

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https://www.readbyqxmd.com/read/28622674/a-two-stage-biomedical-event-trigger-detection-method-integrating-feature-selection-and-word-embeddings
#1
Xinyu He, Lishuang Li, Yang Liu, XiaoMing Yu, Jun Meng
Extracting biomedical events from biomedical literature plays an important role in the field of biomedical text mining, and the trigger detection is a key step in biomedical event extraction. We propose a two-stage method for trigger detection, which divides trigger detection into recognition stage and classification stage, and different features are selected in each stage. In the first stage, we select the features which are more suitable for recognition, and in the second stage, the features that are more helpful to classification are adopted...
June 13, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28600259/local-nearest-neighbors-based-feature-weighting-for-gene-selection
#2
Shuai An, Jun Wang, Jinmao Wei
Selecting functional genes is essential for analyzing microarray data. Among many available feature (gene) selection approaches, the ones on the basis of the large margin nearest neighbor receive more attention due to their low computational costs and high accuracies in analyzing the high-dimensional data. Yet there still exist some problems that hamper the existing approaches in sifting real target genes, including selecting erroneous nearest neighbors, high sensitivity to irrelevant genes, and inappropriate evaluation criteria...
June 7, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28600258/a-self-training-subspace-clustering-algorithm-under-low-rank-representation-for-cancer-classification-on-gene-expression-data
#3
Chun-Qiu Xia, Ke Han, Yong Qi, Yang Zhang, Dong-Jun Yu
Accurate identification of the cancer types is essential to cancer diagnoses and treatments. Since cancer tissue and normal tissue have different gene expression, gene expression data can be used as an efficient feature source for cancer classification. However, accurate cancer classification directly using original gene expression profiles remains challenging due to the intrinsic high-dimension feature and the small size of the data samples. We proposed a new self-training subspace clustering algorithm under low-rank representation, called SSC-LRR, for cancer classification on gene expression data...
June 6, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28622673/exact-algorithms-for-duplication-transfer-loss-reconciliation-with-non-binary-gene-trees
#4
Misagh Kordi, Mukul S Bansal
Duplication-Transfer-Loss (DTL) reconciliation is a powerful method for studying gene family evolution in the presence of horizontal gene transfer. DTL reconciliation seeks to reconcile gene trees with species trees by postulating speciation, duplication, transfer, and loss events. Efficient algorithms exist for finding optimal DTL reconciliations when the gene tree is binary. In practice, however, gene trees are often non-binary due to uncertainty in the gene tree topologies, and DTL reconciliation with non-binary gene trees is known to be NP-hard...
June 1, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28574363/analysis-of-the-genome-sequence-and-prediction-of-b-cell-epitopes-of-the-envelope-protein-of-middle-east-respiratory-syndrome-coronavirus
#5
Qian Xie, Xiaoyan He, Fangji Yang, Xuling Liu, Ying Li, Yujing Liu, ZhengMeng Yang, Jianhai Yu, Bao Zhang, Wei Zhao
The outbreak of Middle East respiratory syndrome-coronavirus (MERS-CoV) in South Korea in April 2015 led to 186 infections and 37 deaths by the end of October 2015. MERS-CoV was isolated from the imported patient in China. The envelope (E) protein, a small structural protein of MERS-CoV, plays an important role in host recognition and infection. To identify the conserved epitopes of the E protein, sequence analysis was performed by comparing the E proteins from 42 MERS-CoV strains that triggered severe pandemics and infected humans in the past...
May 29, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28574365/highly-accurate-and-efficient-data-driven-methods-for-genotype-imputation
#6
Olivia Choudhury, Ankush Chakrabarty, Scott J Emrich
High-throughput sequencing techniques have generated massive quantities of genotype data. Haplotype phasing has proven to be a useful and effective method for analyzing these data. However, the quality of phasing is undermined by the presence of missing information. Imputation provides an effective means of improving the underlying genotype information. For model organisms, imputation can rely on an available reference genotype panel and a physical or genetic map. For non-model organisms, which often do not have a genotype panel, it is important to design an imputation technique that does not rely on reference data...
May 26, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28574364/genome-rearrangement-with-ilp
#7
Tom Hartmann, Nicolas Wieseke, Roded Sharan, Martin Middendorf, Matthias Bernt
The weighted Genome Sorting Problem (wGSP) is to find a minimum-weight sequence of rearrangements operations that transforms a given gene order into another given gene order using rearrangement operations that are associated with a predefined weight. This paper presents a polynomial sized Integer Linear Program - called GeRe-ILP - for solving the wGSP for the following three types of rearrangement operations: inversion, transposition, and inverse transposition. GeRe-ILP uses O(n(3)) variables and O(n(3)) constraints for gene orders of length n...
May 25, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28541905/inferring-gene-species-assignments-in-the-presence-of-horizontal-gene-transfer
#8
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
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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