George Tseng

Integrating Omics Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 23. September 2015
• Englisch
• ISBN-13: 9781316309476
• Artikelnr.: 52952043

Autorenporträt

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.

Inhaltsangabe

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.