Integrating Omics Data
Cambridge University Press (Verlag)
978-1-107-06911-4 (ISBN)
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.
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. Debashis Ghosh completed his Ph.D. in biostatistics from the University of Washington. After serving on the faculty in the Department of Biostatistics at the University of Michigan and in the Department of Statistics at Pennsylvania State University, he is currently Chair and Professor in the Department of Biostatistics and Informatics at the Colorado School of Public Health. His interests in statistical genomics have primarily focused on the development of novel methods for integration of high-throughput data from different platforms. These motivating problems have also led to lines of methodologic research in the areas of multiple comparisons procedures, machine learning techniques and Empirical Bayes procedures. Ghosh is a recipient of several awards including Fellow of the American Statistical Association and the 2013 recipient of the Mortimer Spiegelman Award, for early career contributions of statistics in applied public health problems. Jasmine Zhou completed her Ph.D. at the Swiss Federal Institute of Technology (ETH Zurich), and conducted her post-doc training at Harvard University. She is currently a professor of biological sciences and computer science at the University of Southern California. Dr Zhou is the PI of the NIH center for knowledge base on disease connections within the MAPGen consortium. Dr Zhou heads the laboratory of computational integrative genomics at the University of Southern California, addressing the 'Big Data' challenges brought by the enormous amount of extremely diverse genomic data in public repositories. She was a recipient of several awards including an Alfred Sloan fellowship and a NSF Career award.
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.
| Erscheint lt. Verlag | 23.9.2015 |
|---|---|
| Zusatzinfo | 31 Tables, unspecified; 23 Plates, color; 147 Line drawings, unspecified |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Maße | 156 x 235 mm |
| Gewicht | 820 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Studium ► Querschnittsbereiche ► Epidemiologie / Med. Biometrie | |
| Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie | |
| ISBN-10 | 1-107-06911-4 / 1107069114 |
| ISBN-13 | 978-1-107-06911-4 / 9781107069114 |
| Zustand | Neuware |
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