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E-raamat: Integrating Omics Data

(University of Pittsburgh), (University of Southern California), (Pennsylvania State University)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 23-Sep-2015
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781316288764
  • Formaat - EPUB+DRM
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 23-Sep-2015
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781316288764

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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. 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.

This book provides comprehensive coverage of information integration of omics, experimental data, and databases. It introduces state-of-the-art methods developed by leaders in the field to handle information integration problems of omics data. Popular technologies include microarray, next-generation sequencing, and proteomic experiments.

Muu info

Tutorial chapters by leaders in the field introduce state-of-the-art methods to handle information integration problems of omics data.
Contributors vii
Introduction 1(8)
Part A Horizontal Meta-Analysis
1 Meta-Analysis of Genome-Wide Association Studies: A Practical Guide
9(30)
Wei Chen
2 MetaOmics: Transcriptomic Meta-Analysis Methods for Biomarker Detection, Pathway Analysis and Other Exploratory Purposes
39(29)
SungHwan Kim
Zhiguang Huo
Yongseok Park
George C. Tseng
3 Integrative Analysis of Many Biological Networks to Study Gene Regulation
68(20)
Wenyuan Li
Chao Dai
Xianghong Jasmine Zhou
4 Network Integration of Genetically Regulated Gene Expression to Study Complex Diseases
88(22)
Zhidong Tu
Bin Zhang
Jun Zhu
5 Integrative Analysis of Multiple ChIP-X Data Sets Using Correlation Motifs
110(25)
Hongkai Ji
Yingying Wei
Part B Vertical Integrative Analysis (General Methods)
6 Identify Multi-Dimensional Modules from Diverse Cancer Genomics Data
135(20)
Shihua Zhang
Wenyuan Li
Xianghong Jasmine Zhou
7 A Latent Variable Approach for Integrative Clustering of Multiple Genomic Data Types
155(19)
Ronglai Shen
8 Penalized Integrative Analysis of High-Dimensional Omics Data
174(31)
Jin Liu
Xingjie Shi
Jian Huang
Shuangge Ma
9 A Bayesian Graphical Model for Integrative Analysis of TCGA Data: BayesGraph for TCGA Integration
205(16)
Yanxun Xu
Yitan Zhu
Yuan Ji
10 Bayesian Models for Flexible Integrative Analysis of Multi-Platform Genomics Data
221(21)
Elizabeth J. McGuffey
Jeffrey S. Morris
Ganiraju C. Manyam
Raymond J. Carroll
Veerabhadran Baladandayuthapani
11 Exploratory Methods to Integrate Multisource Data
242(29)
Eric F. Lock
Andrew B. Nobel
Part C Vertical Integrative Analysis (Methods Specialized to Particular Data Types)
12 eQTL and Directed Graphical Model
271(20)
Wei Sun
Min Jin Ha
13 MicroRNAs: Target Prediction and Involvement in Gene Regulatory Networks
291(19)
Panayiotis V. Benos
14 Integration of Cancer Omics Data into a Whole-Cell Pathway Model for Patient-Specific Interpretation
310(27)
Charles Vaske
Sam Ng
Evan Paull
Joshua Stuart
15 Analyzing Combinations of Somatic Mutations in Cancer Genomes
337(25)
Mark D. M. Leiserson
Benjamin J. Raphael
16 A Mass-Action-Based Model for Gene Expression Regulation in Dynamic Systems
362(18)
Guoshou Teo
Christine Vogel
Debashis Ghosh
Sinae Kim
Hyungwon Choi
17 From Transcription Factor Binding and Histone Modification to Gene Expression: Integrative Quantitative Models
380(23)
Chao Cheng
18 Data Integration on Noncoding RNA Studies
403(22)
Zhou Du
Teng Fei
Myles Brown
X. Shirley Liu
Yiwen Chen
19 Drug-Pathway Association Analysis: Integration of High-Dimensional Transcriptional and Drug Sensitivity Profile
425(20)
Cong Li
Can Yang
Greg Hather
Ray Liu
Hongyu Zhao
Index 445
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.