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

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An efficient method for fuzzy identification of regulatory events in gene expression time series data

An efficient method for fuzzy identification of regulatory e...

引用

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

作者： L.G. Volkert N. Malhis Department of Computer Science at Kent State University Kent OH USA Department of Computer Science Kent University Kent OH USA

The increase in the number of time-series gene expression experiments drives an increase in the development of analysis methods that aim to reverse engineer genetic regulatory networks. Methods have previously been developed that test all combinations of gene interactions in a gene expression time-series dataset for those that closely fit a model of gene regulation. We describe an efficient method for reducing the computational cost of this approach by pruning the search space of candidates least likely to fit a regulatory model. Our approach represents changes in expression levels as directional slope information and discards combinations whose slope relationships are most likely outside the scope of the given model of regulatory relationships. We demonstrate a 70% reduction in the computational time required to process three S. cerevisiae microarray time-series datasets.

关键词： Gene expression Data mining Genetics bioinformatics Data analysis Genomics Intelligent networks Time series analysis Reverse engineering Testing

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Protein family classification using structural and sequence information

Protein family classification using structural and sequence ...

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

作者： S.F. Smith Department of Electrical and Computer Engineering Boise State University Boise ID USA

Protein family classification usually relies on sequence information (as in the case of hidden Markov models and position-specific scoring matrices) or on structural information where some sort of average positional error between the atomic locations is used. The positional error method requires that the structure of all the proteins to be classified is known. Sequence methods have the advantage that a much larger number of proteins can be classified (since far more sequences are know than structures). However, sequence methods discard a large amount of useful information contained in the structures of the subset of proteins in the family for which structures are known. A protein family classification system is presented which uses both structural and sequence information and combines this information in a way consistent with fuzzy systems theory. The nonlinear fuzzy-theory-based method is found to perform better than either an equally-weighted linear combination of the sequence and structural information or the sequence information alone.

关键词： Hidden Markov models Protein engineering Databases Fuzzy systems Protein sequence biology computing computational systems biology Computer architecture Topology Amino acids

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A variant of SVM-RFE for gene selection in cancer classification with expression data

A variant of SVM-RFE for gene selection in cancer classifica...

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

作者： K. Duan J.C. Rajapakse Biolnformatics Research Centre Nanyang Technological University Singapore School of Computer Engineering Nanyang Technological University Singapore

Feature selection is a commonly addressed problem in classification. In gene expression-based cancer classification, a large number of genes in conjunction with a small number of samples makes the gene selection problem more important but also more challenging. Support vector machine as a popular classification algorithm, has been successfully used in SVM-RFE method for gene selection. This paper proposes a variant of SVM-RFE to do gene selection for cancer classification with expression data. Multiple support vector machine classifiers from a leave-one-out procedure are used to compute the feature ranking scores. The numerical experiments also show the good and stable performance of the proposed method.

关键词： Cancer Filters Support vector machines Support vector machine classification Gene expression Classification algorithms Costs Diseases computational efficiency Data preprocessing

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A/sup 4/C: an adaptive artificial ants clustering algorithm

A/sup 4/C: an adaptive artificial ants clustering algorithm

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

作者： Xiaohua Xu Ling Chen Y. Chen The Department of Computer Science Yangzhou University Yangzhou China The Department of Computer Science University of Illinois Urbana-Champaign Urbana-Champaign IL USA

With the advance of microarray technology, clustering analysis has become a key tool to make sense of the massive amounts of genes expression data. An artificial ants sleeping model (ASM) and an adaptive artificial ants clustering algorithm (A/sup 4/C) are presented to solve the clustering problem in data mining by simulating the behaviors of social ant colonies. In the ASM model, each datum is represented by an agent. The agents' environment is a two-dimensional grid. In A/sup 4/C, the agents can form into high-quality clusters by making simple moves according to little local information from its neighborhood and the parameters are selected and adjusted adaptively. Experimental results on clustering benchmarks show the ASM and A/sup 4/C are simpler, easier to implement, and more efficient than previous methods.

关键词： Clustering algorithms Algorithm design and analysis Data mining Particle swarm optimization Cadaver Sorting Design optimization High performance computing Genetic expression bioinformatics

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Two-stage support vector machines to protein relative solvent accessibility prediction

Two-stage support vector machines to protein relative solven...

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

作者： M.N. Nguyen J.C. Rajapakse BioInformatics Research Centre School of Computer Engineering Nanyang Technological University Singapore Singapore Bio lnformatics Research Centre School of Computer Engineering Nanyang Technological University Singapore Singapore

bioinformatics techniques to relative solvent accessibility (RSA) prediction are mostly single-stage approaches; they predict solvent accessibility of proteins by taking into account only the information available in amino acid sequences. We propose to use support vector machines (SVMs) as a second stage following the existing single-stage approaches for RSA prediction problem to improve the accuracy. The purpose of the second stage is to capture the contextual relationship of solvent accessibility elements in a neighborhood in determining the solvent accessibility at a particular site. We demonstrate our approach by introducing SVMs to the output of single-stage SVM classifier. Two-stage SVM approach achieves accuracies up to 90.4% and 90.2% on the Manesh dataset of 215 protein structures and the RS126 dataset of 126 nonhomologous globular proteins, respectively, which are better than the highest reported scores on both datasets to date.

关键词： Support vector machines Proteins Solvents Amino acids Support vector machine classification Neural networks Biochemistry Bayesian methods Chromium Organisms

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Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationships

Gene expression correlation and gene ontology-based similari...

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

作者： H. Wang F. Azuaje O. Bodenreider J. Dopazo School of Computing and Mathematics University of Ulster UK U.S. National Library of Medicine Department of Health & Human Services National Institutes of Health DHHS Bethesda MD USA Bioinformatics Unit Spanish National Cancer Centre Madrid Spain

The gene ontology and annotations derived from the *** genome database were analyzed to calculate functional similarity of gene products. Three methods for measuring similarity (including a distance-based approach) were implemented. Significant, quantitative relationships between similarity and expression correlation of pairs of genes were detected. Using a known gene expression dataset in yeast, this study compared more than three million pairs of gene products on the basis of these functional properties. Highly correlated genes exhibit strong similarity based on information originating from the gene ontology taxonomies. Such a similarity is significantly stronger than that observed between weakly correlated genes. This study supports the feasibility of applying gene ontology-driven similarity methods to functional prediction tasks, such as the validation of gene expression analyses and the identification of false positives in protein interaction studies.

关键词： Gene expression Ontologies Genomics bioinformatics Databases Organisms Proteins Information analysis Vocabulary Mathematics

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Multiple DNA sequence approximate matching

Multiple DNA sequence approximate matching

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

作者： K.M. Kaplan J.J. Kaplan Department of Systems and Computer Science Howard University College of Medicine Washington D.C. DC USA United States Air Force Canada

DNA matching is an important key to understanding genomes, evolution, relationships between organisms, and other concepts in genomics. Yet, comparing DNA is unlike matching typed words to a dictionary of words as there is no "true" spelling for DNA. Therefore, approximate matching algorithms must be used. There are many algorithms that can compare two DNA sequences, but when multiple sequences are to be compared, the matching becomes more difficult. Comparing multiple sequences of DNA can be performed using different known methods. These methods include dynamic programming, star alignments, tree alignments, and others, which are usually based on dynamic programming. The method proposed here is a novel method to compare all strings, not merely all strings to one, as in the star alignment method, and it does so in a different way. The proposed method, the Kaplan multiple sequence algorithm, or KMS, separates the multidimensional search area so that comparisons can be performed in parallel. This paper discusses the problem of comparing multiple sequences and introduces this new novel method to match multiple DNA strings.

关键词： DNA Sequences Dynamic programming Organisms Dictionaries Databases Genomics bioinformatics Approximation methods Biomedical computing

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A comparison of sequence alignment algorithms for measuring secondary structure similarity

A comparison of sequence alignment algorithms for measuring ...

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

作者： L.G. Volkert D.A. Stoffer Department of Computer Science Kent University Kent OH USA Department of Computer Science Kent State University Kent OH USA

Methods for protein secondary structure prediction have improved significantly in recent years. This has lead to enhanced protein homology modeling efforts. Protein homology modeling involves the subtask of identifying a set of homologous proteins from a protein database when given as input the amino acid sequence of a query protein, with the ultimate goal of using the resulting set of homologous proteins as a starting point for predicting the 3D structure of the query protein. Previous work has indicated that improvements can be made when combining secondary structure sequence alignment using a 3-state structure symbol alphabet together with primary amino acid sequence alignment methods. These approaches typically use a local alignment algorithm. We compare the performance of several dynamic programming alignment algorithms on the task of aligning secondary structure sequences using an 8-state secondary structure alphabet. Our results indicate that the typical use of a local alignment algorithm may not be best when aligning protein secondary structure information.

关键词： Databases Dynamic programming Protein engineering Coils Amino acids Computer science Decision support systems ieee members Predictive models Heuristic algorithms

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Secret sequence comparison in distributed computing environments by interval sampling

Secret sequence comparison in distributed computing environm...

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

作者： K. Kurata V. Breton H. Nakamura Research Advanced Science and Technology University of Tokyo Tokyo Japan Laboratory de Physique Corpusculaire de Clemont-Fermnd Centre National de la Recherche Scientifique (CNRS) Aubiere France Research Advvnced Science and Technology University of Tokyo Japan

Once a new gene has been sequenced, it must be verified whether or not it is similar to previously sequenced genes. In many cases, the organization that sequenced a potentially novel gene needs to keep the sequence itself in confidence. However, to compare the potentially novel sequence with known sequences, it must either be sent as a query to public databases, or these databases must be downloaded onto a local computer. In both cases, the potentially new sequence is exposed to the public. In this work, we propose a new method, called interval sampling, to compare sequences without leaking exact information about the new sequence. In order to keep the exact sequence information secret, this method samples intervals (subsequences) from a sequence, and these intervals are hashed. The hashed data are open to the public to verify the novelty of the sequence. We find that this method works well in parallel in a distributed computing environment, such as the Grid. The experimental results for 19797 *** genes and 25000 *** genes show that the parallel implementation of this method performs reasonably well in terms of speed and memory usage.

关键词： Distributed computing Sampling methods Sequences Databases Contracts Costs Genomics bioinformatics Search methods IP networks

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High sensitivity technique for translation initiation site detection

High sensitivity technique for translation initiation site d...

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

作者： L.S. Ho J.C. Rajapakse Bioinformatica Research Center School of Computer Engineering Nanyang Technological University Singapore Singapore

Low-order Markov models are insufficient to represent hidden and complex features surrounding translation initiation sites (TISs). We present a neural network approach for detecting TISs of eukaryotes that combines lower-order models carrying biological knowledge, with nonlinearity, to capture higher-order nucleotide correlations at TISs and in the surrounding coding and noncoding regions. The model consists of Markov chain models of low-order, a protein encoding model, neural networks, and a ribosome scanning model. The Markov models capture the local interaction of coding and noncoding regions while the protein encoding model utilizes evolutionary information of proteins to encode the downstream coding sequence of the TIS. With the incorporation of ribosome scanning model, the present method allows for incorporating the biological contextual information of potential TIS into the neural network based prediction system, thereby increasing number of correct recognitions considerably. A 3-fold cross-validation evaluation on the Pedersen and Nielsen dataset yielded an average 93.8% of sensitivity and 96.9% of specificity, indicating superior accuracy of the present system to the previous highest measures of 88.5% of sensitivity and 963% of specificity.

关键词： Biological system modeling Sequences Neural networks Genomics bioinformatics Proteins DNA Biological information theory Encoding Multi-layer neural network

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