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Academic & Professional Books  Reference  Data Analysis & Modelling  Bioinformatics

Integrating Omics Data

By: George Tseng(Author), Debashis Ghosh(Author), Xianghong Jasmine Zhou(Author)
488 pages, 23 colour & 147 b/w illustrations, 31 tables
Integrating Omics Data
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  • Integrating Omics Data ISBN: 9781107069114 Hardback Apr 2016 Usually dispatched within 6 days
Selected version: £88.99
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About this book

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 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. Integrating Omics Data 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
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

Customer Reviews


George Tseng completed his ScD in biostatistics with a concentration in genomics from the Harvard School of Public Health. He is currently a Professor of Biostatistics, Human Genetics, Computational and Systems Biology, and the Director of the Center for Statistical Genomics at the University of Pittsburgh. His research interests focus on statistical and computational method development for analyzing high-throughput omics data.

Debashis Ghosh's current research interests include high-dimensional genomic data analyses, semiparametric models, and survival analysis.

Jasmine Zhou is currently a Professor of Biology and Computer Science at the University of Southern California. Her research focuses on developing computational and statistical methods for the integrative analysis of large-scale and heterogeneous data. She regularly serves as a panelist for NIH and NSF grant review panels, and is on the organization and program committees of numerous international conferences on computational biology.

- Wei Chen
- Sunghwan Kim
- Zhiguang Huo
- Yongseok Park
- George C. Tseng
- Wenyuan Li
- Chao Dai
- Xianghong Jasmine Zhou
- Zhidong Tu
- Bin Zhang
- Jun Zhu
- Hongkai Ji
- Yingying Wei
- Shihua Zhang
- Ronglai Shen
- Jin Liu
- Xingjie Shi
- Jian Huang
- Shuangge Ma
- Yanxun Xu
- Yitan Zhu
- Yuan Ji
- Veera Baladandayuthapani
- Eric F. Lock
- Andrew B. Nobel
- Wei Sun
- Min Jin Ha
- Panayiotis V. Benos
- Charles Vaske
- Sam Ng
- Evan Paull
- Joshua Stuart
- Mark D. M. Leiserson
- Benjamin J. Raphael
- Guoshou Teo
- Christine Vogel
- Debashis Ghosh
- Sinae Kim
- Hyungwon Choi
- Chao Cheng
- Zhou Du
- Teng Fei
- Myles Brown
- X. Shirley Liu
- Yiwen Chen
- Cong Li
- Can Yang
- Greg Hather
- Ray Liu
- Hongyu Zhao

By: George Tseng(Author), Debashis Ghosh(Author), Xianghong Jasmine Zhou(Author)
488 pages, 23 colour & 147 b/w illustrations, 31 tables
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