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IEEE/ACM Transactions on Computational Biology and Bioinformatics

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https://www.readbyqxmd.com/read/28092574/dynamics-in-epistasis-analysis
#1
Aseel Awdeh, Hilary Phenix, Mads Kaern, Theodore Perkins
Finding regulatory relationships between genes, including the direction and nature of influence between them, is a fundamental challenge in the field of molecular genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. While classical epistasis analysis has yielded deep insights on numerous genetic pathways, it is not without limitations...
January 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28092573/environment-sensitivity-based-cooperative-co-evolutionary-algorithms-for-dynamic-multi-objective-optimization
#2
Biao Xu, Yong Zhang, Dunwei Gong, Yinan Guo, Miao Rong
Dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for solving DMOPs. In this strategy, a new method that groups decision variables is first proposed, in which all the decision variables are partitioned into two subcomponents according to their interrelation with environment...
January 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28092572/pspel-in-silico-prediction-of-self-interacting-proteins-from-amino-acids-sequences-using-ensemble-learning
#3
Jian-Qiang Li, Zhu-Hong You, Xiao Li, Ming Zhong, Xing Chen
Self interacting proteins (SIPs) play an important role in various aspects of the structural and functional organization of the cell. Detecting SIPs is one of the most important issues in current molecular biology. Although a large number of SIPs data has been generated by experimental methods, wet laboratory approaches are both time-consuming and costly. In addition, they yield high false negative and positive rates. Thus, there is a great need for in silico methods to predict SIPs accurately and efficiently...
January 10, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28026781/exploring-consensus-rna-substructural-patterns-using-subgraph-mining
#4
Qingfeng Chen, Chaowang Lan, Baoshan Chen, Lusheng Wang, Jinyan Li, Chengqi Zhang
Frequently recurring RNA structural motifs play important roles in RNA folding process and interaction with other molecules. Traditional index-based and shape-based schemas are useful in modeling RNA secondary structures but ignore the structural discrepancy of individual RNA family member. Further, the in-depth analysis of underlying substructure pattern is insufficient due to varied and unnormalized substructure data. This prevents us from understanding RNAs functions and their inherent synergistic regulation networks...
December 26, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28029629/discovering-perturbation-of-modular-structure-in-hiv-progression-by-integrating-multiple-data-sources-through-non-negative-matrix-factorization
#5
Sumanta Ray, Ujjwal Maulik
Detecting perturbation in modular structure during HIV-1 disease progression is an important step to understand stage specific infection pattern of HIV-1 virus in human cell. In this article, we proposed a novel methodology on integration of multiple biological information to identify such disruption in human gene module during different stages of HIV-1 infection. We integrate three different biological information: gene expression information, protein-protein interaction information and gene ontology information in single gene meta-module, through non negative matrix factorization (NMF)...
December 20, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28029628/evolutionary-graph-clustering-for-protein-complex-identification
#6
Tiantian He, Keith C C Chan
This paper presents a graph clustering algorithm, called EGCPI, to discover protein complexes in protein-protein interaction (PPI) networks. In performing its task, EGCPI takes into consideration both network topologies and attributes of interacting proteins, both of which have been shown to be important for protein complex discovery. EGCPI formulates the problem as an optimization problem and tackles it with evolutionary clustering. Given a PPI network, EGCPI first annotates each protein with corresponding attributes that are provided in Gene Ontology database...
December 20, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27992348/efficient-quartet-representations-of-trees-and-applications-to-supertree-and-summary-methods
#7
Ruth Davidson, MaLyn Lawhorn, Joseph Rusinko, Noah Weber
Quartet trees displayed by larger phylogenetic trees have long been used as inputs for species tree and supertree reconstruction. Computational constraints prevent the use of all displayed quartets in many practical problems with large numbers of taxa. We introduce the notion of an Efficient Quartet System (EQS) to represent a phylogenetic tree with a subset of the quartets displayed by the tree. We show mathematically that the set of quartets obtained from a tree via an EQS contains all of the combinatorial information of the tree itself...
December 14, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27992347/network-community-detection-based-on-the-physarum-inspired-computational-framework
#8
Chao Gao, Mingxin Liang, Xianghua Li, Zili Zhang, Zhen Wang, Zhili Zhou
Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem...
December 13, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27992346/prediction-of-hiv-drug-resistance-by-combining-sequence-and-structural-properties
#9
Zoya Khalid, Osman Ugur Sezerman
Drug resistance is a major obstacle faced by therapist in treating HIV infected patients. The reason behind these phenomena is either protein mutation or the changes in gene expression level that induces resistance to drug treatments. These mutations affect the drug binding activity, hence resulting in failure of treatment. Therefore, it is necessary to conduct resistance testing in order to carry out HIV effective therapy. This study combines both sequence and structural features for predicting HIV resistance by applying SVM and Random Forests classifiers...
December 13, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27959819/a-new-efficient-algorithm-for-the-frequent-gene-team-problem
#10
Biing-Feng Wang
The focus of this paper is the frequent gene team problem. Given a quorum parameter  and a set of m genomes, the problem is to find gene teams that occur in at least  of the given genomes. In this paper, a new algorithm is presented. Previous solutions are efficient only when  is small. Unlike previous solutions, the presented algorithm does not rely on examining every combination of  genomes. It's time complexity is independent of . Under some realistic assumptions, the practical running time is estimated to be O(m2n2 lg n), where n is the maximum length of the input genomes...
December 8, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27959818/querying-of-disparate-association-and-interaction-data-in-biomedical-applications
#11
Shi Qiao, Mehmet Koyuturk, Meral Z Ozsoyoglu
In biomedical applications, network models are commonly used to represent interactions and higher-level associations among biological entities. Integrated analyses of these interaction and association data has proven useful in extracting knowledge, and generating novel hypotheses for biomedical research. However, since most datasets provide their own schema and query interface, opportunities for exploratory and integrative querying of disparate data are currently limited. In this study, we utilize RDF-based representations of biomedical interaction and association data to develop a querying framework that enables flexible specification and efficient processing of graph template matching queries...
December 8, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28060710/a-review-of-machine-learning-and-statistical-approaches-for-detecting-snp-interactions-in-high-dimensional-genomic-data
#12
Suneetha Uppu, Aneesh Krishna, Raj Gopalan
In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature...
December 2, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28029627/a-memetic-algorithm-for-3-d-protein-structure-prediction-problem
#13
Leonardo Correa, Bruno Borguesan, Camilo Farfan, Mario Inostroza-Ponta, Marcio Dorn
Memetic Algorithms are population-based metaheuristics intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature of the Memetic Algorithms. In this paper, we present a Memetic Algorithm to tackle the three-dimensional protein structure prediction problem. The method uses a structured population and incorporates a Simulated Annealing algorithm as a local search strategy, as well as ad-hoc crossover and mutation operators to deal with the problem...
December 2, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27925595/classification-of-alzheimer-s-disease-using-whole-brain-hierarchical-network
#14
Jin Liu, Min Li, Wei Lan, Fang-Xiang Wu, Yi Pan, Jianxin Wang
Regions of interest (ROIs) based classification has been widely investigated for analysis of brain magnetic resonance imaging (MRI) images to assist the diagnosis of Alzheimer's disease (AD) including its early warning and developing stages, e.g., mild cognitive impairment (MCI) including MCI converted to AD (MCIc) and MCI not converted to AD (MCInc). Since an ROI representation of brain structures is obtained either by pre-definition or by adaptive parcellation, the corresponding ROI in different brains can be measured...
December 2, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27925594/a-memetic-algorithm-for-3-d-protein-structure-prediction-problem
#15
Leonardo Correa, Bruno Borguesan, Camilo Farfan, Mario Inostroza-Ponta, Marcio Dorn
Memetic Algorithms are population-based metaheuristics intrinsically concerned with exploiting all available knowledge about the problem under study. The incorporation of problem domain knowledge is not an optional mechanism, but a fundamental feature of the Memetic Algorithms. In this paper, we present a Memetic Algorithm to tackle the three-dimensional protein structure prediction problem. The method uses a structured population and incorporates a Simulated Annealing algorithm as a local search strategy, as well as ad-hoc crossover and mutation operators to deal with the problem...
December 2, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27925593/a-review-of-machine-learning-and-statistical-approaches-for-detecting-snp-interactions-in-high-dimensional-genomic-data
#16
Suneetha Uppu, Aneesh Krishna, Raj Gopalan
In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature...
December 2, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27913358/conformational-sampling-of-a-biomolecular-rugged-energy-landscape
#17
Jakub Rydzewski, Rafal Jakubowski, Giuseppe Nicosia, Wieslaw Nowak
The protein structure refinement using conformational sampling is important in hitherto protein studies. In this paper we examined the protein structure refinement by means of potential energy minimization using immune computing as a method of sampling conformations. The method was tested on the x-ray structure and 30 decoys of the mutant of [Leu]Enkephalin, a paradigmatic example of the biomolecular multiple-minima problem. In order to score the refined conformations, we used a standard potential energy function with the OPLSAA force field...
December 1, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27913357/introducing-a-stable-bootstrap-validation-framework-for-reliable-genomic-signature-extraction
#18
Nikolaos-Kosmas Chlis, Ekaterini S Bei, Michael Zervakis
The application of machine learning methods for the identification of candidate genes responsible for phenotypes of interest, such as cancer, is a major challenge in the field of bioinformatics. These lists of genes are often called genomic signatures and their linkage to phenotype associations may form a significant step in discovering the causation between genotypes and phenotypes. Traditional methods that produce genomic signatures from DNA Microarray data tend to extract significantly different lists under relatively small variations of the training data...
November 29, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27893399/enumerating-substituted-benzene-isomers-of-tree-like-chemical-graphs
#19
Jinghui Li, Hiroshi Nagamochi, Tatsuya Akutsu
Enumeration of chemical structures is useful for drug design, which is one of the main targets of computational biology and bioinformatics. A chemical graph 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...
November 15, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/27845672/gpu-based-point-cloud-superpositioning-for-structural-comparisons-of-protein-binding-sites
#20
Matthias Leinweber, Thomas Fober, Bernd Freisleben
In this paper, we present a novel approach to solve the labeled point cloud superpositioning problem for performing structural comparisons of protein binding sites. The solution is based on a parallel evolution strategy that operates on large populations and runs on GPU hardware. The proposed evolution strategy reduces the likelihood of getting stuck in a local optimum of the multimodal real-valued optimization problem represented by labeled point cloud superpositioning. The performance of the GPU-based parallel evolution strategy is compared to a previously proposed CPU-based sequential approach for labeled point cloud superpositioning, indicating that the GPU-based parallel evolution strategy leads to qualitatively better results and significantly shorter runtimes, with speed improvements of up to a factor of 1,500 for large populations...
November 7, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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