"A Chapman & Hall book."

Includes bibliographical references and index.

Linux for big data analysis / Shui Qing Ye and Ding-You Li -- Pythong for big data analysis / Dmitry N. Grigoryev -- R for big data analysis / Stephen D. Simon -- Genome-Seq data analysis / Min Xiong, Li Qin Zhang, and Shui Qing Ye -- RNA-seq data analysis / Li Qin Zhang, Min Xiong, Daniel P. Heruth, and Shui Qing Ye -- Microbiome-seq data analysis / Daniel P. Heruth, Min Xiong, and Xun Jiang -- miRNA-seq data analysis / Daniel P. Heruth, Min Xiong, and Guang-Liand Bi -- Methylome-seq data analysis / Chengpeng Bi -- ChIP-seq data analysis / Shui Qing Ye, Li Qin Zhang, and Jiancheng Tu -- Integrating omics data in big data analysis / Li Qin Zhang, Daniel P. Heruth, and Shui Qing Ye -- Pharmacogenetics and genomics / Andrea Gaedigk, Katrin Sangkuhl, and Larisa H. Cavallari -- Exploring de-identified electronic health record data with i2b2 / Mark Hoffman -- Big data and drug discovery / Gerald J. Wyckoff and D. Andrew Skaff -- Literature-based knowledge discovery / Hongfanf Liu and Majid Rastegar-Mojarad -- Mitigating high dimensionality in big data / Deendayal Dinakarpandian.

"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"-- Back cover.