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IEEE/ACM Transactions on Computational Biology and Bioinformatics

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
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
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
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
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
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
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
Xiaoke Ma, Wanxin Tang, Peizhuo Wang, Xingli Guo, Lin Gao
Determining the dynamics of pathways associated with cancer progression is critical for understanding the etiology of diseases. Advances in biological technology have facilitated the simultaneous genomic profiling of multiple patients at different clinical stages, thus generating the dynamic genomic data for cancers. Such data provide enable investigation of the dynamics of related pathways. However, methods for integrative analysis of dynamic genomic data are inadequate. In this study, we develop a novel nonnegative matrix factorization algorithm for dynamic modules (NMF-DM), which simultaneously analyzes multiple networks for the identification of stage-specific and dynamic modules...
November 7, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jayanta Kumar Pal, Shubhra Sankar Ray, Sung-Bae Cho, Sankar K Pal
MicroRNAs (miRNAs) are known as an important indicator of cancers. Presence of cancer can be detected by identifying the responsible miRNAs. A fuzzy-rough entropy measure (FREM) is developed which can rank the miRNAs and thereby identifying the relevant ones. FREM is used to determine the relevance of a miRNA in terms of separability between normal and cancer classes. While computing the FREM for a miRNA, fuzziness takes care of the overlapping between normal and cancer expressions, whereas rough lower approximation determines their class sizes...
November 1, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Anton V Ushakov, Xenia Klimentova, Igor Vasilyev
Recent advances in high-throughput technologies have given rise to collecting large amounts of multidimensional heterogeneous data that provide diverse information on the same biological samples. Integrative analysis of such multisource datasets may reveal new biological insights into complex biological mechanisms and therefore remains an important research field in systems biology. Most of the modern integrative clustering approaches rely on independent analysis of each dataset and consensus clustering, probabilistic or statistical modeling, while flexible distance-based integrative clustering techniques are sparsely covered...
October 27, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Shu-Guang Ge, Junfeng Xia, Wen Sha, Chun-Hou Zheng
One major goal of large-scale cancer omics study is to understand molecular mechanisms of cancer and find new biomedical targets. To deal with the high-dimensional multidimensional cancer omics data (DNA methylation, mRNA expression, etc.), which can be used to discover new insight on identifying cancer subtypes, clustering methods are usually used to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrate these data and map them into an effective low-dimensional subspace...
October 26, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Renu Vyas, Sanket Bapat, Purva Goel, Muthukumarasamy Karthikeyan, Sanjeev S Tambe, Bhaskar D Kulkarni
Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In a case study, a dataset consisting of one hundred and thirty five PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability...
October 26, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Cinzia Pizzi, Mattia Ornamenti, Simone Spangaro, Simona E Rombo, Laxmi Parida
Entropy, being closely related to repetitiveness and compressibility, is a widely used information-related measure to assess the degree of predictability of a sequence. Entropic profiles are based on information theory principles, and can be used to study the under-/over-representation of subwords, by also providing information about the scale of conserved DNA regions. Here we focus on the algorithmic aspects related to entropic profiles. In particular, we propose linear time algorithms for their computation that rely on suffix-based data structures, more specifically on the truncated suffix tree (TST) and on the enhanced suffix array (ESA)...
October 21, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Narayanan Viswanath
In this study, the expected time required to eradicate HIV-1 completely was found as the conditional absorbing time in a finite state space continuous-time Markov chain model. The Markov chain has two absorbing states: one corresponds to HIV eradication and another representing the possible disaster. This method allowed us to calculate the expected eradication time by solving systems of linear equations. To overcome the challenge of huge dimension of the problem, we applied a novel stop and resume technique...
October 20, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Hyunjin Kim, Sang-Min Choi, Sanghyun Park
When a gene shows varying levels of expression among normal people but similar levels in disease patients or shows similar levels of expression among normal people but different levels in disease patients, we can assume that the gene is associated with the disease. By utilizing this gene expression heterogeneity, we can obtain additional information that abets discovery of disease-associated genes. In this study, we used collaborative filtering to calculate the degree of gene expression heterogeneity between classes and then scored the genes on the basis of the degree of gene expression heterogeneity to find "differentially predicted" genes...
October 19, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jiaxiang Huang, Maoguo Gong, Lijia Ma
Molecular interactions data increase exponentially with the advance of biotechnology. This makes it possible and necessary to comparatively analyse the different data at a network level. Global network alignment is an important network comparison approach to identify conserved subnetworks and get insight into evolutionary relationship across species. Network alignment which is analogous to subgraph isomorphism is known to be an NP-hard problem. In this paper, we introduce a novel heuristic Particle-Swarm-Optimization based Network Aligner (PSONA), which optimizes a weighted global alignment model considering both protein sequence similarity and interaction conservations...
October 19, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Alireza Karbalayghareh, Ulisses Braga-Neto, Jianping Hua, Edward R Dougherty
Gene-expression-based phenotype classification is used for disease diagnosis and prognosis relating to treatment strategies. The present paper considers classification based on sequential measurements of multiple genes using gene regulatory network (GRN) modeling. There are two networks, original and mutated, and observations consist of trajectories of network states. The problem is to classify an observation trajectory as coming from either the original or mutated network. GRNs are modeled via probabilistic Boolean networks, which incorporate stochasticity at both the gene and network levels...
October 11, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jun Hu, Yang Li, Ming Zhang, Xibei Yang, Hong-Bin Shen, Dong-Jun Yu
Protein-DNA interactions are ubiquitous in a wide variety of biological processes. Correctly locating DNA-binding residues solely from protein sequences is an important but challenging task for protein function annotations and drug discovery, especially in the post-genomic era where large volumes of protein sequences have quickly accumulated. In this study, we report a new predictor, named TargetDNA, for targeting protein-DNA binding residues from primary sequences. TargetDNA uses a protein's evolutionary information and its predicted solvent accessibility as two base features and employs a centered linear kernel alignment algorithm to learn the weights for weightedly combining the two features...
October 11, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Bin Hu, Xiaowei Li, Shuting Sun, Martyn Ratcliffe
The research detailed in this paper focuses on the processing of Electroencephalography (EEG) data to identify attention during the learning process. The identification of affect using our procedures is integrated into a simulated distance learning system that provides feedback to the user with respect to attention and concentration. The authors propose a classification procedure that combines correlation-based feature selection (CFS) and a k-nearest-neighbor (KNN) data mining algorithm. To evaluate the CFS+KNN algorithm, it was test against CFS+C4...
October 11, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Guillermo Leale, Ariel Baya, Diego Milone, Pablo Granitto, Georgina Stegmayer
Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been already sequenced, the biological functions of all of their genes are still unknown. Since experimentally studying the functions of those genes, one by one, would be unfeasible, new computational methods for gene functions inference are needed. We present here a novel computational approach for inferring biological function for a set of genes with previously unknown function, given a set of genes with well-known information...
October 7, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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