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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501-510 订阅

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computational Identification of Protein-Coding Sequences by Comparative Analysis

Computational Identification of Protein-Coding Sequences by ...

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Arnaud Fontaine Helene Touzet LIFL-UMR CNRS 8022 INRIA Sequoia University of Lille I France

Gene prediction is an essential step in understanding the genome of a species once it has been sequenced. For that, a promising direction in current research on gene finding is a comparative genomics approach. In this paper, we present a novel approach to identifying evolutionarily conserved protein-coding sequences in genomes. The method takes advantage of the specific substitution pattern of coding se- quences together with the consistency of reading frames. It has been implemented in a software called Protea. Large- scale experimentation shows good results. Protea is in- tended to be a useful complement to existing tools based on homology search or statistical properties of the sequences.

关键词： Proteins bioinformatics Genomics Genetic mutations Large-scale systems RNA Biomedical computing Databases Splicing Organisms

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A New Metric to Measure Gene Product Similarity

A New Metric to Measure Gene Product Similarity

引用

ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Sachin Mathur Deendayal Dinakarpandian School of Computing and Engineering University of Missouri Kansas City MO USA

The widespread use of microarray technology and sequencing of genomes has made it increasingly possible to study the cellular sub-systems of organisms. computational techniques applied to sequence data annotated with ontologies such as the gene ontology (GO) aid in understanding regulatory networks of genes. An important related problem is the estimation of the similarity between gene products based on their annotations. We present an approach to compute gene product similarity that takes into account both the hierarchical nature of GO and the co-occurrence of GO terms in annotations. It also accounts for differences in the cardinality of annotations and differences in the frequency of usage of GO terms. We demonstrate the validity of the metric by computing the similarity between gene products in several different contexts. These include the analysis of similarity within a specific signaling pathway, between proteins constituting a sequence family and the comparative evaluation of different clusterings of microarray data.

关键词： Ontologies bioinformatics Biomedical computing Genomics Organisms Signal processing Proteins Gene expression Biomedical measurements Biomedical engineering

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An Intelligent Replication Framework for Data Grids

An Intelligent Replication Framework for Data Grids

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ACS/ieee International conference on Computer systems and Applications

作者： Ali Elghirani Albert Y. Zomaya Riky Subrata Advanced Networks Research Group University of Sydney NSW Australia

Data grid systems are evolving as prominent platforms of choice for many scientific disciplines. Many of the experiments and applications of such disciplines require use of huge amounts of data located at geographically distributed locations. In this paper, we present an intelligent data and replica management framework coupled with computational job scheduling, to provide an integrated environment for efficient access to data and job scheduling. The main goal of our approach is to build a replica management service that integrates replica placement optimization mechanisms, and dynamic replication techniques, coupled with computation and job scheduling algorithms to provide better system performance in data grids. We evaluate our framework on a Data Grid model adopted from the Eu-Data grid project. Our results show promising improvement in the performance of the grid and job execution time.

关键词： High energy physics instrumentation computing Processor scheduling Grid computing Delay Environmental management Distributed computing bioinformatics computational intelligence System performance Wide area networks

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A Framework for Implementing a Network-Based Stochastic Biochemical Simulator on an FPGA

A Framework for Implementing a Network-Based Stochastic Bioc...

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ieee International conference on Field-Programmable Technology (FPT)

作者： Masato Yoshimi Yuri Nishikawa Toshinori Kojima Yasunori Osana Akira Funahashi Noriko Hiroi Yuichiro Shibata Hideki Yamada Hiroaki Kitano Hideharu Amano Dept. of Computer Science Keio University Yokohama Japan ENMBL-EBI Thornton group European Bioinformatics Institude Cambridge UK Dept. of Computer and Information Sciences Nagasaki University Nagasaki Japan Kitano Symbiotic Systems Project Japan Science and Technology Agency Tokyo Japan

This paper studies several designs of network-based FPGA implementation of a stochastic simulation algorithm called the next reaction method, known for its large number of calculation involved. The procedure is divided into several subdivisions which will be implemented as independent modules, and they are connected with configurable interconnection networks so as to provide high throughput. By performing a multi-threading simulation, 3.6 times speedup was obtained compared with an execution on general purpose processors.

关键词： Stochastic processes Field programmable gate arrays Yarn Stochastic systems computational modeling bioinformatics Algorithm design and analysis Circuit simulation Computer science Computer networks

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WI'07/IAT'07/BIBM'07/GrC'07 Joint Keynote: Computer Science as a Lens on the Sciences: The Example of computational Molecular Biology 07

WI'07/IAT'07/BIBM'07/GrC'07 Joint Keynote: Computer Science ...

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ieee/WIC/ACM International conference on Intelligent Agent Technology (IAT)

作者： Richard M. Karp University of California Berkeley California USA

ISBN: (纸本)9780769530277

This talk will trace the growing influence of fundamental ideas from computer science on the nature of research in a number of scientific fields. There is a growing awareness that information processing lies at the heart of the processes studied in fields as diverse as quantum mechanics, statistical physics, nanotechnology, neuroscience, linguistics, economics and sociology. Increasingly, mathematical models in these fields are expressed in algorithmic languages and describe algorithmic processes. The speaker will briefly describe connections between quantum computing and the foundations of quantum mechanics, and between statistical mechanics and phase transitions in computation. He will indicate how the growth of the Web has created new phenomena to be investigated by sociologists and economists. He will then focus in greater detail on computational molecular biology, where the view of living cells as complex information processing systems has become the dominant paradigm, and will discuss specific algorithmic problems arising in the sequencing of genomes, the comparative analysis of the resulting genomic sequences,the modeling of networks of interacting proteins, and the associations between genetic variation and disease.

关键词： Computer science Lenses Biology computing computational biology Quantum computing Quantum mechanics Information processing Cells (biology) Genomics bioinformatics

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A game-theoretic based dynamic stock market modeling & solution

A game-theoretic based dynamic stock market modeling & solut...

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ieee International conference on systems, Man and Cybernetics

作者： Armin Zeinali Ashkan Rahimi-Kian School of electrical engineering (Control & Intelligent Processing Center of Excellence) University of Tehran Tehran Iran

In this paper, we examine the applicability of genetic algorithm (GA) for solving a non-cooperative stock-market game. We formulate a repeated game where some agents compete with one another to maximize their expected profit/wealth. Each agent estimates the future prices of the trading stocks in order to maximize its expected profit function. Each agent has two actions (selling its own stocks or buying other agents' stocks) for maximizing its expected profit function over time. Our stock-price model is linear, stochastic, and controlled by the agents' actions. We use the GA algorithm for each agent to optimally select its actions (selling or buying stock) in the market. To test our algorithm, we simulate a 4-player stock-market game for one hundred rounds.

关键词： Stock markets Genomics bioinformatics Stochastic processes Genetic algorithms Optimization methods Game theory Genetic mutations computational modeling Testing

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Continuation Newton-GMRES Power Flow with Linear and Nonlinear Predictors

Continuation Newton-GMRES Power Flow with Linear and Nonline...

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conference on Power Engineering Large Engineering systems (LESCOPE)

作者： Hiroyuki Mori Kotaro Seki Department of Electronics and Bioinformatics Meiji University Kawasaki Japan

In this paper, a new method is proposed for the continuation power flow in power systems. The objective of the continuation power flow is to draw PV curves through tracing the power flow solution for the change of loads. The use of P V curves is useful for monitoring static voltage instability problems in a way that the margin to the bifurcation point is evaluated quantitatively. This paper proposes a preconditioned continuation power flow method with linear and nonlinear predictors to reduce the computational effort. The proposed method is based on a preconditioned iterative method for solving a set of linear equations in the Newton-Raphson (N-R) method. This paper makes use of the ILU(p) as the preconditioner and generalized minimum residual (GMRES) method as the iterative method. The proposed method is successfully applied to the ieee 118-node system.

关键词： Load flow Power system stability Iterative methods Voltage Polynomials Power systems Jacobian matrices Nonlinear equations Sparse matrices Bifurcation

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Deep Space Network Scheduling Using Evolutionary computational Methods

Deep Space Network Scheduling Using Evolutionary Computation...

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ieee conference on Aerospace

作者： Alexandre Guillaume Seugnwon Lee Yeou-Fang Wang Hua Zheng Robert Hovden Savio Chau Yu-Wen Tung Richard J. Terrile Jet Propulsion Laboratory Pasadena CA USA

The deep space network (DSN) is an international network of antennas that supports all of NASA's deep space missions. With the increasing demand of tracking time, DSN is highly over-subscribed. Therefore, the allocation of the DSN resources should be optimally scheduled to satisfy the requirements of as many missions as possible. Currently, the DSN schedules are manually and iteratively generated through several meetings to resolve conflicts. In an attempt to ease the burden of the DSN scheduling task, we have applied evolutionary computational techniques to the DSN scheduling problem. These methods provide a decision support system by automatically generating a population of optimized schedules under varying conflict conditions. These schedules are used to decide the simplest path to resolve conflicts as new scheduled items are added or changed along the scheduled 26 weeks. This paper presents the specific approach taken to formulate the problem in terms of gene encoding, fitness function, and genetic operations. The genome is encoded such that a subset of the scheduling constraints is automatically satisfied. Several fitness functions are formulated to emphasize different aspects of the scheduling problem. The optimal solutions of the different fitness functions demonstrate the trade-off of the scheduling problem and provide insight into a conflict resolution process.

关键词： Processor scheduling Computer networks Space missions Resource management Decision support systems Optimization methods Encoding Genetics Genomics bioinformatics

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Reconstruction of Genetic Regulatory Networks Based on the Posterior Probabilities of Gene Regulations

Reconstruction of Genetic Regulatory Networks Based on the P...

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International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wentao Zhao Kwadwo Agyepong Erchin Serpedin Edward R. Dougherty Department of Electrical and Computer Engineering Texas A and M University College Station TX USA Translational Genomics Research Institute Phoenix Phoenix AZ USA

Recent advances in high throughput microarray data have enabled the learning of the structure and operation of gene regulatory networks. This paper proposes a novel approach for reconstruction of gene regulatory networks based on the posterior probabilities of gene regulations. Built within the framework of Bayesian statistics and exploiting efficient computational Monte Carlo techniques, the proposed approach prevents the dichotomy of classifying gene interactions as either being connected or disconnected, and thereby it reduces significantly the inference errors. Simulation results corroborate the superior performance of the proposed approach relative to the existing state-of-the-art algorithms.

关键词： Genetics Biological system modeling Steady-state Bayesian methods Biology computing Inference algorithms bioinformatics Large-scale systems DNA Partial differential equations

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Mining and Predicting CpG islands

Mining and Predicting CpG islands

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ieee International conference on Fuzzy systems (FUZZ-ieee)

作者： Christopher Previti Oscar Harari Coral del Val Department of Molecular Biophysics(B020) German Cancer Research Center Heidelberg Germany Departamento de Ciencias de la Computación e Inteligencia Artificial Escuela Tècnica Superior de Ingeniería Informática C/. Daniel Saucedo Aranda s/n Granada Spain

A DNA sequence can be described as a string composed of four symbols: A, T, C and G. Each symbol represents a chemically distinct nucleotide molecule. Combinations of two nucleotides are called dinucleotides and CpG islands represent regions of a DNA sequence, certain substrings, which are enriched in CpG dinucleotides (C followed by G). CpG islands represent a prominent and enigmatic feature of vertebrate genomes. They are associated with the promoters of more than 60% of all human genes and represent a critical target for transcriptional control. Methylation of these CpG islands leads to structural changes in the DNA that stops the expression of any associated gene (gene-silencing). The factors that provoke or impede methylation are currently all but unknown. In general, the maintenance of a particular pattern of methylated CpG dinucleotides represents a critical regulatory system during a host of normal developmental processes, but the erroneous methylation of CpG islands and the resulting gene-silencing can lead to the development of cancer. In this work, we present a novel unsupervised machine learning method that is capable of distinguishing biologically significant classes of CpG islands, including the separation of methylated and unmethylated CpG islands. This method represents an important novel approach that will aid in the computational prediction of methylation, which is commonly used in the pre-selection of worthwhile sequences for methylation experiments.

关键词： DNA Sequences Chemicals Genomics bioinformatics Humans Impedance Cancer Learning systems Biology computing

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