MARC details
| 000 -LEADER |
|---|
| fixed length control field |
04153cam a2200265 i 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
160516t2016flua b 001 0 eng c |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781498724524 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
570.285 |
| Item number |
B4 |
| 245 00 - TITLE STATEMENT |
|---|
| Title |
Big data analysis for bioinformatics and biomedical discoveries |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Abingdon |
| Name of publisher, distributor, etc. |
CRC Press |
| Date of publication, distribution, etc. |
2016 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xix, 273 p. |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Title |
Chapman and Hall/CRC mathematical and computational biology series |
| 9 (RLIN) |
134081 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Table of Contents<br/><br/><br/>1. Commonly Used Tools for Big Data Analysis<br/>2. Linux for Big Data Analysis <br/>3. Shui Qing Ye and Ding-You Li<br/>4. Python for Big Data Analysis <br/>5. Dmitry N. Grigoryev<br/>6. R for Big Data Analysis <br/>7. Stephen D. Simon<br/>8. Next-Generation DNA Sequencing Data Analysis<br/>9. Genome-Seq Data Analysis <br/>10. Min Xiong, Li Qin Zhang, and Shui Qing Ye<br/>11. RNA-Seq Data Analysis <br/>12. Li Qin Zhang, Min Xiong, Daniel P. Heruth, and Shui Qing Ye<br/>13. Microbiome-Seq Data Analysis <br/>14. Daniel P. Heruth, Min Xiong, and Xun Jiang<br/>15. miRNA-Seq Data Analysis <br/>16. Daniel P. Heruth, Min Xiong, and Guang-Liang Bi<br/>17. Methylome-Seq Data Analysis <br/>18. Chengpeng Bi<br/>19. ChIP-Seq Data Analysis <br/>20. Shui Qing Ye, Li Qin Zhang, and Jiancheng Tu<br/>21. Integrative and Comprehensive Big Data Analysis<br/>22. Integrating Omics Data in Big Data Analysis <br/>23. Li Qin Zhang, Daniel P. Heruth, and Shui Qing Ye<br/>24. Pharmacogenetics and Genomics <br/>25. Andrea Gaedigk, Katrin Sangkuhl, and Larisa H. Cavallari<br/>26. Exploring De-Identified Electronic Health Record Data with i2b2 <br/>27. Mark Hoffman<br/>28. Big Data and Drug Discovery <br/>29. Gerald J. Wyckoff and D. Andrew Skaff<br/>30. Literature-Based Knowledge Discovery <br/>31. Hongfang Liu and Majid Rastegar-Mojarad<br/>32. Mitigating High Dimensionality in Big Data Analysis <br/>33. Deendayal Dinakarpandian<br/><br/> |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Features<br/><br/> Covers the most important topics of Big Data analysis in biomedicine and biology<br/> Introduces computing tools for Big Data analysis, such as Linux-based command lines, Python, and R<br/> Presents data analysis pipelines for next-generation DNA sequencing applications, including Genome-seq, RNA-seq, Microbiome-seq, Methylome-seq, miRNA-seq, and ChIP-seq<br/> Shows how to integrate high-dimensional omics data, pharmacogenomics data, electronic medical records, in silico drug findings, and literature-based knowledge for precision medicine<br/><br/>Summary<br/><br/>Demystifies Biomedical and Biological Big Data Analyses<br/><br/>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.<br/><br/>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.<br/><br/>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.<br/><br/><br/>https://www.crcpress.com/Big-Data-Analysis-for-Bioinformatics-and-Biomedical-Discoveries/Ye/p/book/9781498724524 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Bioinformatics |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Medical sciences |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Data processing |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Sequence alignment - Bioinformatics |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Nucleotide sequence |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Data mining |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Big data |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Medical sciences |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ye, Shui Qing |
| Relator term |
Editor |
| 9 (RLIN) |
338757 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Book |
