Integrating Omics Data

Tutorial chapters by leaders in the field introduce state-of-the-art methods to handle information integration problems of omics data.

George Tseng (Author), Debashis Ghosh (Author), Xianghong Jasmine Zhou (Author)

9781107069114, Cambridge University Press

Hardback, published 23 September 2015

476 pages, 147 b/w illus. 23 colour illus. 31 tables
23.5 x 15.6 x 3 cm, 0.82 kg

In 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.

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.

Subject Areas: Life sciences: general issues [PSA], Biology, life sciences [PS], Probability & statistics [PBT], Epidemiology & medical statistics [MBNS]