Big data analysis for bioinformatics and biomedical discoveries
Author(s)
Bibliographic Information
Big data analysis for bioinformatics and biomedical discoveries
（Chapman and Hall/CRC mathematical & computational biology series / series editors Alison M. Etheridge ... [et al.]）
CRC Press/Taylor & Francis Group, c2016
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Note
"A Chapman & Hall book."
Includes bibliographical references and index
Contents of Works
- Linux for big data analysis / Shui Qing Ye and Ding-You Li
- Phython 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-Liang Bi
- Methylome-seq data analysis / Chengpeng Bi
- ChIP-seq data analysis / Li Qin Zhang, Daniel P. Heruth, and Shui Qing Ye
- 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 / Hongfang Liu and Majid Rastegar-Mojarad
- Mitigating high dimensionality in big data analysis / Deendayal Dinakarppandian
Description and Table of Contents
Description
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.
Table of Contents
Commonly Used Tools for Big Data Analysis. Next-Generation DNA Sequencing Data Analysis. Integrative and Comprehensive Big Data Analysis.
by "Nielsen BookData"