This book introduces a number of cutting edge statistical methods which can be used for the analysis of genomic, proteomic and metabolomic data sets. In particular in the field of systems biology, researchers are trying to analyze as much data as possible in a given biological system (such as a cell or an organ). The appropriate statistical evaluation of these large scale data is critical for the correct interpretation and different experimental approaches require different approaches for the statistical analysis of these data. This book is written by biostatisticians and mathematicians but aimed at experimental researcher as well as computational biologists who often lack an appropriate background in statistical analysis.
Author(s): Matthias Dehmer, Frank Emmert-Streib, Armin Graber, Armindo Salvador
Edition: 1
Publisher: Wiley-VCH
Year: 2011
Language: English
Pages: 479
Tags: Медицинские дисциплины;Социальная медицина и медико-биологическая статистика;
CoverPage......Page 1
BookSeries......Page 3
TitlePage......Page 4
CopyRight......Page 5
Contents......Page 6
Preface......Page 18
Contributors......Page 20
Part One: Modeling, Simulation, and Meaning of Gene Networks......Page 26
1 Network Analysis to Interpret Complex Phenotypes......Page 28
2 Stochastic Modeling of Gene Regulatory Networks......Page 38
3 Modeling Expression Quantitative Trait Loci in Multiple Populations......Page 64
Part Two: Inference of Gene Networks......Page 92
4 Transcriptional Network Inference Based on Information Theory......Page 94
5 Elucidation of General and Condition-Dependent Gene Pathways Using Mixture Models and Bayesian Networks......Page 116
6 Multiscale Network Reconstruction from Gene Expression Measurements......Page 130
7 Gene Regulatory Networks Inference: Combining a Genetic Programming and H1 Filtering Approach......Page 158
8 Computational Reconstruction of Protein Interaction Networks......Page 180
Part Three: Analysis of Gene Networks......Page 206
9 What if the Fit is Unfit? Criteria for Biological Systems Estimation Beyond Residual Errors......Page 208
10 Machine Learning Methods for Identifying Essential Genes and Proteins in Networks......Page 226
11 Gene Coexpression Networks for the Analysisof DNA Microarray Data......Page 240
12 Correlation Network Analysis and Knowledge Integration......Page 276
13 Network Screening: A New Method to Identify Active Networks from an Ensemble of Known Networks......Page 306
14 Community Detection in Biological Networks......Page 324
15 On Some Inverse Problems in Generating Probabilistic Boolean Networks......Page 354
16 Boolean Analysis of Gene Expression Datasets......Page 374
Part Four: Systems Approach to Diseases......Page 402
17 Representing Cancer Cell Trajectories in a Phase-Space Diagram......Page 404
18 Protein Network Analysis for Disease Gene Identification and Prioritization......Page 430
19 Pathways and Networks as Functional Descriptors for Human Disease and Drug Response Endpoints......Page 440
Index......Page 468
