Java for Bioinformatics and Biomedical Applications

本书结合行业标准软件工程原则、基因组学、生物信息学及癌症研究,专注于为科学界创建实用工具,解决研究人员日常面临的实际生物医学问题。利用来自NIH、NCI-Center for Bioinformatics等机构的技术。

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This book aims to combine industry standard software engineering and design principles, genomics and bioinformatics and cancer research. It focuses on creating and integrating practical, useful tools for the scientific community in the context of real-life, real-value biomedical problems that researchers face on a routine basis, rather than being just a didactic exercise in learning a programming platform. The book leverages technologies for molecular biology, genomics and bioinformatics and cancer research developed by the NIH, NCI-Center for Bioinformatics (NCICB), the National Center for Biotechnology Information (NCBI, a division of the National Library of Medicine (NLM) at the NIH) and Stanford University.
http://rapidshare.com/files/27016856/b-673b01.zip
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Series: Chapman & Hall/CRC Mathematical and Computational Biology Hardcover: 294 pages Publisher: Chapman and Hall/CRC (December 22, 2015) Language: English ISBN-10: 1498724523 ISBN-13: 978-1498724524 Demystifies Biomedical and Biological Big Data Analyses Big Data Analysis for Bioinformatics and Biomedical Discoveries provides a practical guide to the nuts and bolts of Big Data, enabling you to quickly and effectively harness the power of Big Data to make groundbreaking biological discoveries, carry out translational medical research, and implement personalized genomic medicine. Contributing to the NIH Big Data to Knowledge (BD2K) initiative, the book enhances your computational and quantitative skills so that you can exploit the Big Data being generated in the current omics era. The book explores many significant topics of Big Data analyses in an easily understandable format. It describes popular tools and software for Big Data analyses and explains next-generation DNA sequencing data analyses. It also discusses comprehensive Big Data analyses of several major areas, including the integration of omics data, pharmacogenomics, electronic health record data, and drug discovery. Accessible to biologists, biomedical scientists, bioinformaticians, and computer data analysts, the book keeps complex mathematical deductions and jargon to a minimum. Each chapter includes a theoretical introduction, example applications, data analysis principles, step-by-step tutorials, and authoritative references
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