Integrating Omics Data
Integrating Omics Data
Debashis Ghosh, Pennsylvania State University
Xianghong Jasmine Zhou, University of Southern California
£98.99
GBPIn most modern biomedical research projects, application of high-throughput genomic, proteomic, and transcriptomic experiments has gradually become an inevitable component. Popular technologies include microarray, next generation sequencing, mass spectrometry and proteomics assays. 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
August 2015Adobe eBook Reader
9781316309476
0 pages
0kg
147 b/w illus. 23 colour illus. 31 tables
This ISBN is for an eBook version which is distributed on our behalf by a third party.
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.
