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

共 878 条记录，以下是91-100 订阅

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Toward a realistic model for gene network evolution

Toward a realistic model for gene network evolution

引用

7th ieee International conference on bioinformatics and Bioengineering, BIBE

作者： Chen, Wenhai Jiang, Tianzi Xia, Tian Gu, Xun National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing 100080 China Center for Bioinformatics and Computational Biology Iowa State University Ames IA 50011 United States Center for Bioinformatics and Biological Statistics Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States

We investigated how to establish a realistic model for the evolutionary emergence of gene network that is scale-free. After theoretical analysis and compute simulation, we have demonstrated the evolutionary principles... 详细信息

ISBN: (纸本)1424415098

关键词： Genes

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Efficient basin hopping in the protein energy surface 2012

Efficient basin hopping in the protein energy surface

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2012 ieee International conference on bioinformatics and Biomedicine, BIBM2012

作者： Olson, Brian Shehu, Amarda Department of Computer Science George Mason University Fairfax VA 22030 United States Department of Bioinformatics and Computational Biology George Mason University Fairfax VA 22030 United States Department of Bioengineering George Mason University Fairfax VA 22030 United States

ISBN: (纸本)9781467325585

The vast and rugged protein energy surface can be effectively represented in terms of local minima. The basin-hopping framework, where a structural perturbation is followed by an energy minimization, is particularly suited to obtaining this coarse-grained representation. Basin hopping is effective for small systems both in locating lower-energy minima and obtaining conformations near the native structure. The efficiency decreases for large systems. Our recent work improves efficiency on large systems through molecular fragment replacement. In this paper, we conduct a detailed investigation of two components in basin hopping, perturbation and minimization, and how they work in concert to affect the sampling of near-native local minima. We show that controlling the magnitude of perturbation jumps is related to the ability to effectively steer the exploration towards conformations near the protein native state. In minimization, we show that a simple greedy search is just as effective as Metropolis Monte Carlo-based minimization. Finally, we show that an evolutionary-inspired approach based on the Pareto front is particularly effective in reducing the ensemble of sampled local minima and obtains a simpler representation of the probed energy surface. © 2012 ieee.

关键词： Proteins

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I/O induced scalability limits of bioinformatics applications

I/O induced scalability limits of bioinformatics application...

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7th ieee International conference on bioinformatics and Bioengineering, BIBE

作者： Henschel, Robert Müller, Matthias S. 01062 Dresden Germany

ISBN: (纸本)1424415098

The growing size of sequence, protein and other biological databases results in an increased computational complexity of the analysis process. Often parallelization is the only solution to limit the turnaround time within reasonable limits. Most scalability studies focus on the parallel algorithm and the resulting communication and synchronization patterns of the implementations. In this paper we examine to what extend I/O bottlenecks limit the scalability on current and future architectures. We study the behavior of two different bioinformatics applications (THREADER, HMMER) and show that these applications are representatives of two different classes with distinct I/O profiles and demands. ©2007 ieee.

关键词： Scalability

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Accelerating Needleman-Wunsch Global Alignment Algorithm with GPUs 12

Accelerating Needleman-Wunsch Global Alignment Algorithm wit...

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12th ieee/ACS International conference of Computer systems and Applications (AICCSA)

作者： Fakirah, Maged Shehab, Mohammed A. Jararweh, Yaser Al-Ayyoub, Mahmoud Jordan Univ Sci & Technol Irbid Jordan

ISBN: (纸本)9781509004782

Over the recent decades, bioinformatics has acquired a major concern due to the rapid growth in biological data that includes protein structures and genome sequences. Many considerable efforts have been conducted by computer scientists, mathematicians and biologists to coup with complex biological problems such as sequence alignment problem, using several techniques to formulate/model the targeted biological problems as computational problems and design algorithms to solve them in an accurate and efficient manner. NeedlemanWunsch algorithm as well as other alignment algorithms have been the subject of many studies to improve their performance due to their importance and the large scale of the data they have to handle (e.g., aligning strings of hundreds of thousands of characters). Approaches included a mixture of different parallel implementations using specialized hardware such as Graphical Processing Units (GPUs) and a vectorized approach of reading and processing the input data. In this work, a parallel implementation of NW algorithm is presented using GPU due to its efficiency and high speed, to solve the slowness problem associated with this algorithm when processing large data sets, as well as to enhance the performance of the algorithm especially when processing vectors of adjacent cells parallel to the matrix miner diagonal. The experiments show that the proposed implementation improves the performance of the algorithm by 99%.

关键词： bioinformatics

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Gene teams with relaxed proximity constraint

Gene teams with relaxed proximity constraint

引用

2005 ieee computational systems bioinformatics conference, CSB 2005

作者： Kim, Sun Choi, Jeong-Hyeon Yang, Jiong School of Informatics Center for Genomics and Bioinformatics Indiana University IN 47408 United States School of Informatics Indiana University IN 47408 United States EECS Case Western Reserve Univ. OH 44106 United States

ISBN: (纸本)0769523447

Functionally related genes co-evolve, probably due to the strong selection pressure in evolution. Thus we expect that they are present in multiple genomes. Physical proximity among genes, known as gene team, is a very useful concept to discover functionally related genes in multiple genomes. However, there are also many gene sets that do not preserve physical proximity. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraint. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. Especially, analysis of gene clusters that belong to B. subtilis and E. coli demonstrated that our model predicted many experimentally verified operons and functionally related clusters. Our program is fast enough to provide a sevice on the web at http://***/platcom/. Users can select any combination of 97 genomes to predict gene teams. © 2005 ieee.

关键词： Genes

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Nucleotide composition bias in high throughput gene expression measurement methods 13

Nucleotide composition bias in high throughput gene expressi...

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13th ieee International conference on Signal Processing and Communication systems (ICSPCS)

作者： Jaksik, Roman Lalik, Anna Puszynski, Krzysztof Silesian Tech Univ Inst Automat Control Gliwice Poland

ISBN: (纸本)9781728121949

Studies of gene expression levels are known to be affected by biases related to the experimental procedure. Nucleotide composition of the studied genes significantly affects the efficiency of biochemical reactions used to prepare the material for study which translates to the measured expression levels. Data pre-processing methods aim at reducing such differences and the use of replicate samples is expected to increase the data quality, however all commonly used methods suffer from the reproducibility problems. In this work we utilize information provided by over two thousand experiments in order to explain the origin of nucleotide composition bias, highlight the magnitude of the problem and show its consequences for the interpretation of gene expression studies. We show that despite data standardization and the use of sample replicates the effect can have significant impact on the detection of differentially expressed genes between analyzed conditions, affecting interpretation of results from such studies.

关键词： RNA sequencing oligonucleotide microarrays detection of differentially expressed genes bioinformatics

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computational grid-based 3-tier ART1 data mining for bioinformatics applications

引用

3rd International conference on Fuzzy systems and Knowledge Discovery

作者： Cho, Kyu Cheol Park, Da Hye Lee, Jong Sik Inha Univ Sch Comp Sci & Engn Nam Ku Inchon 402751 South Korea

ISBN: (纸本)3540459162

computational Grid technology has been noticed as an issue to solve large-scale bioinformatics-related problems and improves data accuracy and processing speed on multiple computation platforms with distributed bioDATA sets. This paper focuses on a GPCR data mining processing which is an important bioinformatics application. This paper proposes a Grid-based 3-tier ART1 classifier which operates an ART1 clustering data mining using grid computational resources with distributed GPCR data sets. This Grid-based 3-tier ART1 classifier is able to process a large-scale bioinformatics application in guaranteeing high bioDATA accuracy with reasonable processing resources. This paper evaluates performance of the Grid-based ART1 classifier in comparing to the ART1-based classifier and the ART1 optimum classifier. The data mining processing time of the Grid-based ART1 classifier is 18% data mining processing time of the ART1 optimum classifier and is the 12% data mining processing time of the ART1-based classifier. And we evaluate performance of the Grid-based 3-tier ART1 classifier in comparing to the Grid-based ART1 classifier. As data sets become larger, data mining processing time of the Grid-based 3-tier ART1 classifier more decrease than that of the Grid-based ART1 classifier. computational Grid in bioinformatics applications gives a great promise of high performance processing with large-scale and geographically distributed bioDATA sets.

关键词： Data mining

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A Multi-objective Cluster-based Biased Random-Key Genetic Algorithm with Online Parameter Control Applied to Protein Structure Prediction 17

A Multi-objective Cluster-based Biased Random-Key Genetic Al...

引用

17th conference on Computer Science and Intelligence systems (FedCSIS)

作者： Marchi, Felipe Parpinelli, Rafael Stubs Santa Catarina State Univ Appl Comp Grad Program Joinville SC Brazil

ISBN: (数字)9788396242396

ISBN: (纸本)9788396242396

The protein structure prediction problem is one of the most important bioinformatics problems. computational methods can be used to approach this problem and de novo methods are able to generate protein structures without the need of having known similar structures to the predicted protein. These methods transform the prediction problem into an optimization problem, using optimization models that combine different energy functions and high-level information. These models usually have only a single optimization objective. However, it is known that this single objective optimization approach may harm the optimization search due to the existence of conflicts between the different terms that compose the optimization objective. The proposed model has three objectives: energy function, secondary structure, and contact maps. A multi-objective Biased RandomKey Genetic Algorithm (BRKGA) with online parameter control, named MOBO, is proposed as the optimizer. The final predictor comprises two phases of the MOBO algorithm and selects a final structure using the MUFOLD-CL clustering method. Results obtained demonstrated that the proposed predictor generated highly competitive results with the literature.

关键词： bioinformatics Multi-objective Optimization Evolutionary Computation Clustering Online Parameter Tuning Protein Structure Prediction

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A Literature Search Tool for Identifying Disease-associated Genes using Hidden Markov Model 1

A Literature Search Tool for Identifying Disease-associated ...

引用

First International conference on computational systems and Communications (ICCSC)

作者： Sreekala, S. Nazeer, K. A. Abdul Natl Inst Technol Dept Comp Sci & Engn Calicut Kerala India

ISBN: (纸本)9781479960132

Understanding the role of genetics is very important for the in-depth study of a disease. Even though lots of information about gene-disease association is available, it is difficult even for an expert user to manually extract it from the huge volume of literature. Therefore, this work introduces a novel extraction tool that can identify disease associated genes from the literature using text-mining algorithm. Here, Hidden Markov Model is combined with a rule-based Named Entity Recognition approach to identify gene symbols from the literature. This will predict the good candidate genes for the disease which will help in the further analysis of the disease.

关键词： bioinformatics data mining diseases genetics hidden Markov models search problems text analysis disease-associated gene identification extraction tool gene-disease association hidden Markov model literature search tool rule-based named entity recognition approach text-mining algorithm Abstracts bioinformatics Diseases Genetics Hidden Markov models Navigation Probability

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Protein-protein interaction network inference from multiple kernels with optimization based on random walk by linear programming

Protein-protein interaction network inference from multiple ...

引用

ieee International conference on bioinformatics and Biomedicine - Medical Informatics and Decision Making

作者： Huang, Lei Liao, Li Wu, Cathy H. Univ Delaware Dept Comp & Informat Sci Newark DE 19716 USA Univ Delaware Ctr Bioinformat & Computat Biol Newark DE 19711 USA

ISBN: (纸本)9781467367981

Reconstruction of PPI networks is a central task in systems biology, and inference from multiple heterogeneous data sources offers a promising computational approach to making de novo PPI prediction by leveraging complementary information and the partial network structure. However, how to quickly learn weights for heterogeneous data sources remains a challenge. In this work, we developed a method to infer de novo PPIs by combining multiple data sources represented in kernel format and obtaining optimal weights based on random walk over the existing partial network. Our proposed method utilizes Baker algorithm and the training data to construct a transition matrix which constrains how a random walk would traverse the partial network. Multiple heterogeneous features for the proteins in the network, including gene expression and Pfam domain profiles, are then combined into the form of a weighted kernel, which provides a new "adjacency matrix" for the whole network but is required to comply with the transition matrix on the part of the training subnetwork. This requirement is met by adjusting the weights to minimize the element-wise difference between the transition matrix and the weighted kernel. The minimization problem is solved by linear programming. The weighted kernel is then transformed to regularized Laplacian (RL) kernel to infer missing or new edges in the PPI network. The results on synthetic data and real data from Yeast show that the accuracy of PPI prediction measured by AUC is increased by up to 19% as compared to a control method without using optimal weights. Moreover, the weights learned by our method Weight Optimization by Linear Programming (WOLP) are very consistent with that learned by sampling, and can provide insights into the relations between PPIs and various feature kernels, thereby improving PPI prediction.

关键词： bioinformatics Electronics packaging Genomics Kernel Proteins Weight measurement Data sources systems biology Proteins Random walk linear programming Genomics Seeds pulse position indicator Kernel S matrix Electronics packaging Weighing bioinformatics

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