High Performance and High Throughput Bioinformatics: Accelerate Time Consuming Bioinformatics Applications and Extract Knowledge from Large Amount of Biological Data - Couverture souple

Yang, Luobin

 
9783659148644: High Performance and High Throughput Bioinformatics: Accelerate Time Consuming Bioinformatics Applications and Extract Knowledge from Large Amount of Biological Data

Synopsis

Bioinformatics is an exciting new interdiscipline. This book introduces how computer hardware platforms (computer cluster, FPGA, and GPU) can be used to accelerate time-consuming bioinformatics applications and also introduces two bioinformatics data analysis pipelines to extract knowledge from large amount of biological data. This book has the following contents: A parallel phylogenetic bootstrap analysis program that can greatly reduce the computational time when running one a computer cluster, a parallel k-means data clustering algorithm that can run on a Field Programmable Gate Array (FPGA), a parallel k-means data clustering algorithm that can run on a modern Graphics Processing Unit (GPU), a data analysis pipeline that can apply gene set enrichment analysis on non-human organisms to predict human health consequences of a given treatment based on the responses in non-human organisms, and a data analysis pipeline to detect differentially expressed genes in organisms without a reference genome sequence using transcriptome analysis by sequencing (RNA-seq)

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Présentation de l'éditeur

Bioinformatics is an exciting new interdiscipline. This book introduces how computer hardware platforms (computer cluster, FPGA, and GPU) can be used to accelerate time-consuming bioinformatics applications and also introduces two bioinformatics data analysis pipelines to extract knowledge from large amount of biological data. This book has the following contents: A parallel phylogenetic bootstrap analysis program that can greatly reduce the computational time when running one a computer cluster, a parallel k-means data clustering algorithm that can run on a Field Programmable Gate Array (FPGA), a parallel k-means data clustering algorithm that can run on a modern Graphics Processing Unit (GPU), a data analysis pipeline that can apply gene set enrichment analysis on non-human organisms to predict human health consequences of a given treatment based on the responses in non-human organisms, and a data analysis pipeline to detect differentially expressed genes in organisms without a reference genome sequence using transcriptome analysis by sequencing (RNA-seq)

Biographie de l'auteur

Luobin Yang has been working in the bioinformatics field since 2003 after he got his Master's degree in bioinformatics. He is a research assistant professor at Idaho State University working on high performance bioinformatics and data mining for biomedical data.

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