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Bioinformatics : high performance parallel computer architectures / edited by Bertil Schmidt.

Contributor(s): Material type: TextTextSeries: Embedded multi-core systemsPublication details: Boca Raton : CRC Press, 2011.Description: xiv, 356 p. : ill. ; 25 cmISBN:
  • 9781439814888
Subject(s): DDC classification:
  • 572.80285   22
Contents:
Algorithms for Bioinformatics, B. Schmidt -- Introduction to GPGPUs and Massively Threaded Programming, R.M. Farber -- FPGA: Architecture and Programming, D. Maskell -- Parallel Algorithms for Alignments on the Cell BE, A. Sarje and S. Aluru -- Orchestrating the Phylogenetic Likelihood Function on Emerging Parallel Architectures, A. Stamatakis -- Parallel Bioinformatics Algorithms for CUDA-enabled GPUs, Y. Liu, B. Schmidt, and D. Maskell -- CUDA Error Correction Method for High-Throughput Short-Read Sequencing Data, H. Shi, W. Liu, and B. Schmidt -- FPGA Acceleration of Seeded Similarity Searching, A.C. Jacob, J.M. Lancaster, J.D. Buhler, and R.D. Chamberlain -- Seed-Based Parallel Protein Sequence Comparison Combining Multithreading, GPU, and FPGA Technologies, D. Lavenier and V.-H. Nguyen -- Database Searching with Profi le Hidden Markov Models on Reconfi gurable and Many-Core Architectures, J.P.Walters, V. Chaudhary, and B. Schmidt -- COPACOBANA: A Massively Parallel FPGA-Based Computer Architecture, M. Schimmler, L. Wienbrandt, T. Güneysu, and J. Bissel -- Accelerating String Set Matching for Bioinformatics Using FPGA Hardware, Y.S. Dandass -- Reconfi gurable Neural System and its Application to Dimeric Protein Binding Site Identification, F. Lin and M. Stepanova -- Parallel FPGA Search Engine for Protein Identification, D. Coca, I. Bogdan, and R.J. Beynon.
Summary: New sequencing technologies have broken many experimental barriers to genome scale sequencing, leading to the extraction of huge quantities of sequence data. This expansion of biological databases established the need for new ways to harness and apply the astounding amount of available genomic information and convert it into substantive biological understanding.
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Item type Current library Call number Copy number Status Date due Barcode
Books Books Main library General Stacks 572.80285 / SC.B 2011 (Browse shelf(Opens below)) 1 Available 011584
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572.80285 / OR.B 2008 Bioinformatics : 572.80285 / PO.B 2007 Bioinformatics / 572.80285 / RA.B 2009 Bioinformatics / 572.80285 / SC.B 2011 Bioinformatics : 572.80285 / SE.A 2008 Applied bioinformatics : 572.80285 / SH.B 2009 Bioinformatics : 572.80285 / ST.E 2010 Exploring bioinformatics :

Includes bibliographical references and index.

Algorithms for Bioinformatics, B. Schmidt -- Introduction to GPGPUs and Massively Threaded Programming, R.M. Farber -- FPGA: Architecture and Programming, D. Maskell -- Parallel Algorithms for Alignments on the Cell BE, A. Sarje and S. Aluru -- Orchestrating the Phylogenetic Likelihood Function on Emerging Parallel Architectures, A. Stamatakis -- Parallel Bioinformatics Algorithms for CUDA-enabled GPUs, Y. Liu, B. Schmidt, and D. Maskell -- CUDA Error Correction Method for High-Throughput Short-Read Sequencing Data, H. Shi, W. Liu, and B. Schmidt -- FPGA Acceleration of Seeded Similarity Searching, A.C. Jacob, J.M. Lancaster, J.D. Buhler, and R.D. Chamberlain -- Seed-Based Parallel Protein Sequence Comparison Combining Multithreading, GPU, and FPGA Technologies, D. Lavenier and V.-H. Nguyen -- Database Searching with Profi le Hidden Markov Models on Reconfi gurable and Many-Core Architectures, J.P.Walters, V. Chaudhary, and B. Schmidt -- COPACOBANA: A Massively Parallel FPGA-Based Computer Architecture, M. Schimmler, L. Wienbrandt, T. Güneysu, and J. Bissel -- Accelerating String Set Matching for Bioinformatics Using FPGA Hardware, Y.S. Dandass -- Reconfi gurable Neural System and its Application to Dimeric Protein Binding Site Identification, F. Lin and M. Stepanova -- Parallel FPGA Search Engine for Protein Identification, D. Coca, I. Bogdan, and R.J. Beynon.

New sequencing technologies have broken many experimental barriers to genome scale sequencing, leading to the extraction of huge quantities of sequence data. This expansion of biological databases established the need for new ways to harness and apply the astounding amount of available genomic information and convert it into substantive biological understanding.

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