Integrating Omics Data - Debashis Ghosh

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

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

Sommaire:
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.