Integrating Omics Data
Integrating Omics Data
Debashis Ghosh , Pennsylvania State University
Xianghong Jasmine Zhou , University of Southern California
$134.00
USDIn most modern biomedical research projects, application of high-throughput genomic, proteomic and transcriptomic experiments has gradually become an inevitable component. Popular technologies include microarray and next-generation sequencing such as CHiP and RNA-Seq. As the technologies have become mature and the price affordable, omics data are rapidly generated and the problem of information integration and modeling of multi-lab and/or multi-omics data is becoming a growing one in the bioinformatics field.
This book provides comprehensive coverage of these topics, and will have a long-lasting impact on this evolving subject. Each chapter, written by a leader in the field, introduces state-of-the-art methods to handle information integration, experimental data, and database problems of omics data.
- Introduces state-of-the-art methods for omics data integration
- Written by world-class leaders in the field
- Covers practical methods and software that meet biological needs
Product details
September 2015Hardback
9781107069114
476 pages
235 × 156 × 30 mm
0.82kg
147 b/w illus. 23 colour illus. 31 tables
Available
Table of Contents
- 1. Meta-analysis of genome-wide association studies: a practical guide Wei Chen, Dajiang Liu and Lars Fritsche
- 2. Integrating omics data: statistical and computational methods Sunghwan Kim, Zhiguang Huo, Yongseok Park and George C. Tseng
- 3. Integrative analysis of many biological networks to study gene regulation Wenyuan Li, Chao Dai and Xianghong Jasmine Zhou
- 4. Network integration of genetically regulated gene expression to study complex diseases Zhidong Tu, Bin Zhang and Jun Zhu
- 5. Integrative analysis of multiple ChIP-X data sets using correlation motifs Hongkai Ji and Yingying Wei
- 6. Identify multi-dimensional modules from diverse cancer genomics data Shihua Zhang, Wenyuan Li and Xianghong Jasmine Zhou
- 7. A latent variable approach for integrative clustering of multiple genomic data types Ronglai Shen
- 8. Penalized integrative analysis of high-dimensional omics data Jin Liu, Xingjie Shi, Jian Huang and Shuangge Ma
- 9. A Bayesian graphical model for integrative analysis of TCGA data: BayesGraph for TCGA integration Yanxun Xu, Yitan Zhu and Yuan Ji
- 10. Bayesian models for integrative analysis of multi-platform genomics data Veera Baladandayuthapani
- 11. Exploratory methods to integrate multi-source data Eric F. Lock and Andrew B. Nobel
- 12. eQTL and Directed Graphical Model Wei Sun and Min Jin Ha
- 13. microRNAs: target prediction and involvement in gene regulatory networks Panayiotis V. Benos
- 14. Integration of cancer omics data on a whole-cell pathway model for patient-specific interpretation Charles Vaske, Sam Ng, Evan Paull and Joshua Stuart
- 15. Analyzing combinations of somatic mutations in cancer genomes Mark D. M. Leiserson and Benjamin J. Raphael
- 16. A mass action-based model for gene expression regulation in dynamic systems Guoshou Teo, Christine Vogel, Debashis Ghosh, Sinae Kim and Hyungwon Choi
- 17. From transcription factor binding and histone modification to gene expression: integrative quantitative models Chao Cheng
- 18. Data integration on non-coding RNA studies Zhou Du, Teng Fei, Myles Brown, X. Shirley Liu and Yiwen Chen
- 19. Drug-pathway association analysis: integration of high-dimensional transcriptional and drug sensitivity profile Cong Li, Can Yang, Greg Hather, Ray Liu and Hongyu Zhao.
