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检索条件"任意字段=IEEE Computational Systems Bioinformatics Conference"

共 881 条记录，以下是541-550 订阅

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Locality and Parallelism Optimization for Dynamic Programming Algorithm in bioinformatics

Locality and Parallelism Optimization for Dynamic Programmin...

引用

Supercomputing conference

作者： Guangming Tan Shengzhong Feng Ninghui Sun Graduate School of Chinese Academy of Sciences China Key Laboratory of Computer System and Architecture Institute of Computing Technology Chinese Academy and Sciences China

Dynamic programming has been one of the most efficient approaches to sequence analysis and structure prediction in biology. However, their performance is limited due to the drastic increase in both the number of biological data and variety of the computer architectures. With regard to such predicament, this paper creates excellent algorithms aimed at addressing the challenges of improving memory efficiency and network latency tolerance for nonserial polyadic dynamic programming where the dependences are nonuniform. By relaxing the nonuniform dependences, we proposed a new cache oblivious scheme to enhance its performance on memory hierarchy architectures. Moreover we develop and extend a tiling technique to parallelize this nonserial polyadic dynamic programming using an alternate block-cyclic mapping strategy for balancing the computational and memory load, where an analytical parameterized model is formulated to determine the tile volume size that minimizes the total execution time and an algorithmic transformation is used to schedule the tile to overlap communication with computation to further minimize communication overhead on parallel architectures. The numerical experiments were carried out on several high performance computer systems. The new cache-oblivious dynamic programming algorithm achieve 2-10 speedup and the parallel tiling algorithm with communication-computation overlapping shows a desired potential for fine-grained parallel computing on massively parallel computer systems

关键词： Dynamic programming Heuristic algorithms bioinformatics Concurrent computing Parallel processing Computer architecture Tiles Sequences Delay Memory architecture

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Assessing the Codetermination of Gene Transcriptional States Using Both Superscalar and Vector Multiple Processor systems

Assessing the Codetermination of Gene Transcriptional States...

引用

Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： R.L. Martino D.E. Russ C.A. Johnson Division of Computational Bioscience Center for Information TechnologyDepartment of Health and Human Services National Institutes of Health DHHS Bethesda MD USA

An important goal of functional genomics is to develop methods for determining ways in which individual actions of genes are integrated in the cell. One way of gaining insight into a gene's role in cellular activity is to study its expression pattern in a variety of circumstances and contexts, as it responds to its environment and to the action of other genes. Microarrays provide large-scale surveys of gene expression in which transcript levels can be determined for thousands of genes simultaneously. The coefficient of determination (CoD) has been proposed for the analysis of gene interaction via multivariate expression arrays. Parallel computing is essential to the application of the CoD to a large set of genes because of the large number of expression-based functions that must be statistically designed and compared. The results of the calculation of the CoD for a large set of genes with multiple superscalar processors are presented. A proposal for calculating the CoD with multiple vector processors is described. Multiple vector processor systems offer the potential to greatly reduce the time to calculate the CoD for a large set of genes

关键词： Gene expression DNA Genomics bioinformatics Large-scale systems Parallel processing Vector processors Biological information theory Information technology Humans

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The Cancer Biomedical Informatics Grid (caBIGTM)

The Cancer Biomedical Informatics Grid (caBIGTM)

引用

Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： D. Fenstermacher C. Street T. McSherry V. Nayak C. Overby M. Feldman Department of Pathology and Laboratory Medicine University of Pennsylvania USA Biomedical Informatics Facility Abramson Cancer Center University of Pennsylvania USA

The Cancer Biomedical Informatics Grid (caBIGtrade) is a new project initiated by the National Cancer Institute to create a computational network connecting scientists and institutions to enable the sharing of data and the use of common analytical tools. The emergence of genomics and proteomics high-throughput technologies are creating a paradigm shift in biomedical research from small independent labs to large teams of researchers exploring entire genomes and proteomes and how they relate to disease. caBIGtrade is developing new software and modifying existing software within Clinical Trials Management systems, Tissue Banks and Pathology Tools and Integrated Cancer Research tools to manage the huge volume of data being generated and to facilitate collaboration across the broad spectrum of cancer research

关键词： Cancer Biomedical informatics Collaborative software Genomics bioinformatics Software tools Software development management Technology management Biomedical computing Computer networks

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Controlling Nonlinear Dynamic systems with Projection Pursuit Learning

Controlling Nonlinear Dynamic Systems with Projection Pursui...

引用

International ieee conference on Intelligent systems, IS

作者： Clodoaldo A. M. Lima Pablo A. D. Castro Andre L. V. Coelho Cynthia Junqueira Fernando J. Von Zuben Laboratory of Bioinformatics and Bio-inspired Computing (LBiC) State University of Campinas-UNICAMP Sao Paulo Brazil Applied Informatics (MIA) University of Fortaleza-UNIFOR Fortaleza Ceara Brazil Laboratory of Signal Processing for Communications (DSPCom) State University of Campinas-UNICAMP Sao Paulo Brazil

Projection pursuit learning (PPL) refers to a well-known constructive learning algorithm characterized by a very efficient and accurate computational procedure oriented to nonparametric regression. It has been employed as a means to counteract some problems related to the design of artificial neural network (ANN) models, namely, the estimation of a (usually large) number of free parameters, the proper definition of the model's dimension, and the choice of the sources of nonlinearities (activation functions). In this work, the potentials of PPL are exploited through a different perspective, namely, in designing one-hidden-layer feedforward ANNs for the adaptive control of nonlinear dynamic systems. For such purpose, the proposed methodology is divided into three stages. In the first, the model identification process is undertaken. In the second, the ANN structure is defined according to an offline control setting. In these two stages, the PPL algorithm estimates not only the optimal number of hidden neurons but also the best activation function for each node. The final stage is performed online and promotes a fine-tuning in the parameters of the identification model and the controller. Simulation results indicate that it is possible to design effective neural models based on PPL for the control of nonlinear multivariate systems, with superior performance when compared to benchmarks

关键词： Nonlinear control systems Control systems Artificial neural networks Aerodynamics Neurons Pursuit algorithms Adaptive control Nonlinear dynamical systems Laboratories System identification

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Evaluating Distance Measures for RNA Motif Search

Evaluating Distance Measures for RNA Motif Search

引用

Congress on Evolutionary Computation

作者： J. Schonfeld D. Ashlock Bioinformatics and Computational Biology Iowa State University Ames IA USA Mathematics and Statistics University of Guelph Guelph ONT Canada

This paper extends an earlier study which outlined a bioinformatic pipeline for exploratory search for RNA motifs incorporating both primary and secondary structure. The pipeline is applied to three data sets, one of which is a larger version of that used in the earlier study. Instead of a single method of estimating the distance between RNA folds four distance measures were tested. The data sets are: a set of random control sequences, a set of synthetic sequences with simple designed folds, and the iron response element data set for which actual biological RNA folds are available. The pipeline demonstrates the ability to produce clusters that contain known motifs in the biological data and those designed into the synthetic data. The results for the distance measures varies substantially and one of the measures, difference in energy, is found to be too simplistic to be useful for differentiating motifs. The other three distance measures all demonstrate some degree of merit. At the heart of the pipeline is a non-linear projection algorithm that uses evolutionary computation to display the intra-RNA-fold distances so that the various distance measures can be visually compared. While the performance of this algorithm is acceptable, suggestions for improving it are made.

关键词： RNA Pipelines Energy measurement bioinformatics Testing Biological control systems Iron Heart Projection algorithms Evolutionary computation

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Motif Discovery as a Multiple-Instance Problem

Motif Discovery as a Multiple-Instance Problem

引用

International conference on Tools for Artificial Intelligence (ICTAI)

作者： Ya Zhang Yixin Chen Xiang Ji Department of EECS University of Kansas USA Department of CIS University of Mississippi USA Yahoo Inc. India

Motif discovery from bio sequences, a challenging task both experimentally and computationally, has been a topic of immense study in recent years. In this paper, we formulate the motif discovery problem as a multiple-instance problem and employ a multiple-instance learning method, the MILES method, to identify motif from biological sequences. Each sequence is mapped into a feature space defined by instances in training sequences with a novel instance-bag similarity measure. We employ I-norm SVM to select important features and construct classifiers simultaneously. These high-ranked features correspond to discovered motifs. We apply this method to discover transcriptional factor binding sites in promoters, a typical motif finding problem in biology, and show that the method is at least comparable to existing methods

关键词： Genetic mutations DNA bioinformatics Random sequences computational Intelligence Society Biology computing Learning systems Extraterrestrial measurements Support vector machines Support vector machine classification

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Accomplishments and Challenges of Protein Ontology

Accomplishments and Challenges of Protein Ontology

引用

ieee Annual International Computer Software and Applications conference (COMPSAC)

作者： E. Chang F.K. Hussain T.S. Dillon A.S. Sidhu Fac. of Inf. Technol. Technol. Sydney Univ. NSW

Recent progress in proteomics, computational biology, and ontology development has presented an opportunity to investigate protein data sources from a unique perspective that is, examining protein data sources through structure and hierarchy of protein ontology (PO). Various data mining algorithms and mathematical models provide methods for analyzing protein data sources; however, there are two issues that need to be addressed: (1) the need for standards for defining protein data description and exchange and (2) eliminating errors which arise with the data integration methodologies for complex queries. Protein ontology is designed to meet these needs by providing a structured protein data specification for protein data representation. Protein ontology is a standard for representing protein data in a way that helps in defining data integration and data mining models for protein structure and function. We report here our development of PO; a semantic heterogeneity framework based on relationships between PO concepts; and analysis of resultant PO data of human proteins. We also talk in this paper briefly about our ongoing work of designing a trustworthy framework around PO

关键词： Ontologies Protein engineering Databases Biology Australia Data mining bioinformatics Information technology Information systems Proteomics

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The center for computational biology

The center for computational biology

引用

ieee computational systems bioinformatics conference

作者： Toga, AW Univ Calif Los Angeles Sch Med Lab Neuro Imaging Los Angeles CA 90024 USA

not avaliable

ISBN: (纸本)0769523447

not avaliable

关键词： computational Biology bioinformatics

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PLATCOM: A platform for computational comparative genomics on the Web

PLATCOM: A platform for computational comparative genomics o...

引用

2005 ieee computational systems bioinformatics conference, Workshops and Poster Abstracts

作者： Choi, Kwangmin Choi, Jeong-Hyeon Saple, Amit Wang, Zhiping Lee, Jason Kim, Sun School of Informatics Center for Genomics and Bioinformatics Indiana University IN 47408 United States

No abstract available

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Cluster utility: A new metric for clustering biological sequences

Cluster utility: A new metric for clustering biological sequ...

引用

2005 ieee computational systems bioinformatics conference, Workshops and Poster Abstracts

作者： Lee, Jason Kim, Sun School of Informatics Indiana University Bloomington IN 47404 United States Center for Genomics and Bioinformatics Indiana University Bloomington IN 47404 United States

No abstract available

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