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Big data analysis for bioinformatics and biomedical discoveries

Contributor(s): Ye, Shui Qing [Editor].
Material type: materialTypeLabelBookSeries: Chapman and Hall/CRC mathematical and computational biology series. Publisher: Abingdon CRC Press 2016Description: xix, 273 p.ISBN: 9781498724524.Subject(s): Bioinformatics | Medical sciences | Data processing | Sequence alignment - Bioinformatics | Nucleotide sequence | Data mining | Big data | Medical sciencesDDC classification: 570.285 Summary: Features Covers the most important topics of Big Data analysis in biomedicine and biology Introduces computing tools for Big Data analysis, such as Linux-based command lines, Python, and R Presents data analysis pipelines for next-generation DNA sequencing applications, including Genome-seq, RNA-seq, Microbiome-seq, Methylome-seq, miRNA-seq, and ChIP-seq Shows how to integrate high-dimensional omics data, pharmacogenomics data, electronic medical records, in silico drug findings, and literature-based knowledge for precision medicine Summary 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. https://www.crcpress.com/Big-Data-Analysis-for-Bioinformatics-and-Biomedical-Discoveries/Ye/p/book/9781498724524
List(s) this item appears in: VR_Healthcare Analytics | Big data | VR_Data Analytics, Data Visualization and Big Data
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Non-fiction 570.285 B4 (Browse shelf) Available 193442

Table of Contents


1. Commonly Used Tools for Big Data Analysis
2. Linux for Big Data Analysis
3. Shui Qing Ye and Ding-You Li
4. Python for Big Data Analysis
5. Dmitry N. Grigoryev
6. R for Big Data Analysis
7. Stephen D. Simon
8. Next-Generation DNA Sequencing Data Analysis
9. Genome-Seq Data Analysis
10. Min Xiong, Li Qin Zhang, and Shui Qing Ye
11. RNA-Seq Data Analysis
12. Li Qin Zhang, Min Xiong, Daniel P. Heruth, and Shui Qing Ye
13. Microbiome-Seq Data Analysis
14. Daniel P. Heruth, Min Xiong, and Xun Jiang
15. miRNA-Seq Data Analysis
16. Daniel P. Heruth, Min Xiong, and Guang-Liang Bi
17. Methylome-Seq Data Analysis
18. Chengpeng Bi
19. ChIP-Seq Data Analysis
20. Shui Qing Ye, Li Qin Zhang, and Jiancheng Tu
21. Integrative and Comprehensive Big Data Analysis
22. Integrating Omics Data in Big Data Analysis
23. Li Qin Zhang, Daniel P. Heruth, and Shui Qing Ye
24. Pharmacogenetics and Genomics
25. Andrea Gaedigk, Katrin Sangkuhl, and Larisa H. Cavallari
26. Exploring De-Identified Electronic Health Record Data with i2b2
27. Mark Hoffman
28. Big Data and Drug Discovery
29. Gerald J. Wyckoff and D. Andrew Skaff
30. Literature-Based Knowledge Discovery
31. Hongfang Liu and Majid Rastegar-Mojarad
32. Mitigating High Dimensionality in Big Data Analysis
33. Deendayal Dinakarpandian

Features

Covers the most important topics of Big Data analysis in biomedicine and biology
Introduces computing tools for Big Data analysis, such as Linux-based command lines, Python, and R
Presents data analysis pipelines for next-generation DNA sequencing applications, including Genome-seq, RNA-seq, Microbiome-seq, Methylome-seq, miRNA-seq, and ChIP-seq
Shows how to integrate high-dimensional omics data, pharmacogenomics data, electronic medical records, in silico drug findings, and literature-based knowledge for precision medicine

Summary

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.


https://www.crcpress.com/Big-Data-Analysis-for-Bioinformatics-and-Biomedical-Discoveries/Ye/p/book/9781498724524

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