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检索条件"任意字段=IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology"

共 1499 条记录，以下是931-940 订阅

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An automatic gene ontology software tool for bicluster and cluster comparisons

An automatic gene ontology software tool for bicluster and c...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Fadhl M. Al-Akwaa Yasser M. Kadah Biomedical Engineering Department Cairo University Egypt Biomedical Engineering Department Center of Informatics Science (CIS) Nile University Egypt

We propose an Automatic Gene Ontology (AGO) software as a flexible, open-source Matlab software tool that allows the user to easily compare the results of the bicluster and cluster methods. This software provides several methods to differentiate and compare the results of candidate algorithms. The results reveal that bicluster/cluster algorithms could be considered as integrated modules to recover the interesting patterns in the microarray datasets. The further application of AGO could to solve the dimensionality reduction of the gene regulatory networks.

关键词： Ontologies Software tools Clustering algorithms bioinformatics Open source software Genomics Software algorithms Genetics Gene expression Biological information theory

来源：评论

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Comparison of feature selection techniques for viral DNA replication origin prediction

Comparison of feature selection techniques for viral DNA rep...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Raul Cruz-Cano Ming-Ying Leung Texas A and M University Texarkana TX USA University of Texas El Paso El Paso TX USA

As the replication of their DNA genomes is a central step in the reproduction of many viruses, procedures to find replication origins, which are initiation sites of the DNA replication process, are of great importance for controlling the growth and spread of such viruses. Existing computational methods for viral replication origin prediction have mostly been designed to use only the composition of a region of viral DNA to predict if such region is an ORI or not. This paper proposes the application of several feature selection techniques to help find the most significant features of the replication origins in herpesviruses. The results suggest that features based on the relative positions of the regions in the genomes containing replication origins and the information about the subfamily of the virus can be highly useful features to be incorporated into the computational tools for viral replication origin prediction.

关键词： DNA Genomics bioinformatics Sequences Support vector machines Viruses (medical) Machine learning Artificial neural networks Input variables Accuracy

来源：评论

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Clustering incorporating shortest paths identifies relevant modules in functional interaction networks

Clustering incorporating shortest paths identifies relevant ...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Jennifer Hallinan Matthew Pocock Stephen Addinall David A Lydall Anil Wipat School of Computing Sciences University of Newcastle Newcastle-upon-Tyne UK Center of the Integrated Systems Biology of Ageing and Nutrition Newcastle-upon-Tyne UK

Many biological systems can be modeled as networks. Hence, network analysis is of increasing importance to systems biology. We describe an evolutionary algorithm for selecting clusters of nodes within a large network based upon network topology together with a measure of the relevance of nodes to a set of independently identified genes of interest. We apply the algorithm to a previously published integrated functional network of yeast genes, using a set of query genes derived from a whole genome screen of yeast strains with a mutation in a telomere uncapping gene. We find that the algorithm identifies biologically plausible clusters of genes which are related to the cell cycle, and which contain interactions not previously identified as potentially important. We conclude that the algorithm is valuable for the querying of complex networks, and the generation of biological hypotheses.

关键词： Clustering algorithms Fungi Biological systems Biological system modeling Systems biology Evolutionary computation Network topology Genomics bioinformatics Capacitive sensors

来源：评论

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Improved prediction of trans-membrane spans in proteins using an artificial neural network

Improved prediction of trans-membrane spans in proteins usin...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Julia Koehler Ralf Mueller Jens Meiler Center of Structural Biology Vanderbilt University Nashville TN USA

Tools for the identification of trans-membrane spans from the protein sequence are widely used in the experimental community. computational structural biology seeks to increase the prediction accuracy of such methods since they represent a first step towards membrane protein tertiary structure prediction from the amino acid sequence. We introduce a predictor that is able to identify trans-membrane spans from the sequence of a protein. The novelty of the approach presented here is the simultaneous prediction of trans-membrane spanning alpha-helices and beta-strands within a single tool. An artificial neural network was trained on databases of 102 membrane proteins and 3499 soluble proteins. Prediction accuracies of up to 92% for soluble residues, 75% for residues in the interface, and 73% for TM residues are achieved. On average the algorithm predicts 79% of the residues correctly which is a substantial improvement from a previously published implementation which achieved 57% accuracy (Koehler et al., Proteins: Structure, Function, and bioinformatics, 2008). The algorithm was applied to four membrane proteins to illustrate the applicability to both alpha-helical bundles and beta-barrels.

关键词： Artificial neural networks Biomembranes Accuracy Protein sequence biology computing computational biology Amino acids Databases Prediction algorithms bioinformatics

来源：评论

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Data synthesis and tool development for exploring imaging genomic patterns

Data synthesis and tool development for exploring imaging ge...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Sungeun Kim Li Shen Andrew J. Saykin John D. West IU Center of Neuroimaging Department of Radiology and the Center of Computational Biology and Bioinformatics Indiana University School of Medicine Indianapolis IN USA

Recent advances in brain imaging and high throughput genotyping techniques enable new approaches to study the influence of genetic variation on brain structure and function. However, major computational challenges are bottlenecks for comprehensive joint analysis of these high-dimensional image and genomic data. We report our initial progress in developing an imaging genomic browsing system for integrated exploration of neuroimaging and genomic data. We describe a method for synthesizing a set of realistic neuroimaging and genomic data, where the relationships between imaging phenotypes and genotypes are known. This data set is used to demonstrate the functionality of our system, which is designed for effectively exploring the neuroanatomical distribution of statistical results that measure the associations between brain imaging phenotypes and genotypes on a genome-wide scale. The proposed system has substantial potential for enabling discovery of important imaging genomic associations through visual evaluation and can be extended towards several directions.

关键词： Genomics bioinformatics Magnetic resonance imaging Brain Genetics Neuroimaging Throughput Data visualization Diffusion tensor imaging Image analysis

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A Simple Algorithm for (l, d) Motif Search1

A Simple Algorithm for (l, d) Motif Search1

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Dolly Sharma Sanguthevar Rajasekaran Computer Science & Engineering University of Connecticut Storrs CT USA

Extracting meaningful patterns from voluminous amount of biological data is a very big challenge. Motifs are biological patterns of great interest to biologists. There are different versions of the Motif Finding Problem. In this paper we concentrate on the Planted (l, d) Motif Search Problem. There have been numerous algorithms designed to solve this problem. Many instances of the Planted (l, d) Motif Problem have been identified as challenging instances. The algorithm proposed here is an extension of PMS3. It uses a very simple approach and solves challenging instances ((21, 8), for example) that have not been reported solved before in the literature. We also propose a new algorithm PMS3p. We expect PMS3p to be significantly faster than PMS3.

关键词： Approximation algorithms Hamming distance Genomics bioinformatics Voting Computer science Medical services Data mining Search problems Algorithm design and analysis

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Predicting protein-protein interactions using numerical associational features

Predicting protein-protein interactions using numerical asso...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Waleed Aljandal William H. Hsu Jing Xia Department of Computing and Information Sciences Kansas State University Manhattan KS USA

We investigate the problem of predicting protein-protein interaction (PPI) using numerical features constructed from parent-child relation of a partial network constructed from known protein interactions. For each pair of proteins, we use a validation-based approach to normalize these features, which are based on association rule interestingness measures. The primary contribution of this work is the parametric normalization formula we derive and calibrate using data for the PPI task. This formula improves basic interestingness measures through taking sizes of itemset into account. Our derived itemset size-sensitive measures consider those rare but significant relationships among the children and the parents of set of proteins. We evaluate our work using k-nearest neighbor and rule-based classification approach.

关键词： Proteins Association rules Data mining Itemsets Gene expression Size measurement bioinformatics Statistics Computer networks Genomics

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A novel LDA and PCA-based hierarchical scheme for metagenomic fragment binning

A novel LDA and PCA-based hierarchical scheme for metagenomi...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Hao Zheng Hongwei Wu Department of Electrical and Computer Engineering Georgia Institute of Technology USA

Metagenomics is to study microorganisms by directly extracting and cloning their DNAs from the environment without lab cultivation or isolation of individual genomes. Assembling of metagenomic DNA fragments is very much like the overlap-layout-consensus procedure for assembling isolated genomes, but is augmented by an additional binning step to differentiate scaffolds, contigs and unassembled reads into various taxonomic groups. In this paper, we employed oligonucleotide frequencies as the features and developed a hierarchical scheme for the challenging task of binning short metagenome fragments, in which principal component analysis (PCA) was implemented to reduce the high dimensionality of the feature space, and linear discriminant analysis (LDA) was used for the local classifier design. Simulation results and comparisons with a non-hierarchical classifier in silico were presented to demonstrate the effectiveness and performance of the proposed PCA and LDA-based hierarchical scheme. The HIER package for this study is available upon request.

关键词： Linear discriminant analysis Principal component analysis Genomics bioinformatics Assembly Microorganisms Cloning DNA Frequency Packaging

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Simulation of the impact of retroviruses on genome organization of an artificial organism

Simulation of the impact of retroviruses on genome organizat...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Daniel Ashlock Wendy Ashlock National Science and Engineering Research Council of Canada Canada

Retroviruses are RNA viruses whose RNA can be transformed into DNA and inserted in the genome of a host cell. This study prototypes a simulation environment in which a simple artificial organism with a variable-size genome evolves with and without retroviruses. The simulated organisms are called grid walkers. They are evolved to efficiently occupy space on a two-dimensional grid. Grid walkers develop a self-organized genome structure with analogs to biological introns and exons. The introduction of retroviruses is found to cause several significant changes: the rate of genome growth is increased, the number of exons is increased, mean exon size is decreased, and fitness is retarded. The grid walkers are found to evolve heritable fitness in preference to high fitness (ldquosurvival of the flattestrdquo), a process that is enhanced when retroviruses are present.

关键词： Genomics bioinformatics Organisms Biological system modeling Humans DNA RNA Evolutionary computation Genetics Immune system

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Steady-state analysis of genetic regulatory networks modeled by nonlinear ordinary differential equations

Steady-state analysis of genetic regulatory networks modeled...

引用

ieee symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Haixin Wang Lijun Qian Edward R. Dougherty Mathematics and Computer Science Fort Valley State University Fort Valley GA USA Department of Electrical Engineering Prairie View A and M University Prairie View TX USA Computational Biology Division Translational Genomics Research Institute Phoenix Phoenix AZ USA

Although Ordinary Differential Equations (ODEs) have been used to model Genetic Regulatory Networks (GRNs) in many previous works, their steady-state behaviors are not well studied. However, a phenotype corresponds to a steady-state gene expression pattern and steady-state analysis of GRNs can provide valuable information on the stability of the GRNs, insights into cellular regulatory mechanisms underlying disease development as well as possible interventions for disease control. In this study, the steady-state behaviors of the nonlinear GRN models are analyzed based on time series data. The steady-state solutions and stability of nonlinear GRNs including polynomial model, sigmoidal model and S-system model are discussed in details.

关键词： Steady-state Genetics Differential equations Diseases Gene expression Information analysis Pattern analysis Stability analysis Time series analysis Polynomials

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