Gene Network Inference: Verification of Methods for Systems Genetics Data

✍ Scribed by Andrea Pinna, Nicola Soranzo, Alberto de la Fuente, Ina Hoeschele (auth.), Alberto de la Fuente (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2013
• Tongue: English
• Leaves: 135
• Edition: 1
• Category: Library

✦ Synopsis

This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians.

✦ Table of Contents

Front Matter....Pages i-xi
Simulation of the Benchmark Datasets....Pages 1-8
A Panel of Learning Methods for the Reconstruction of Gene Regulatory Networks in a Systems Genetics Context....Pages 9-31
Benchmarking a Simple Yet Effective Approach for Inferring Gene Regulatory Networks from Systems Genetics Data....Pages 33-47
Differential Equation Based Reverse-Engineering Algorithms: Pros and Cons....Pages 49-61
Gene Regulatory Network Inference from Systems Genetics Data Using Tree-Based Methods....Pages 63-85
Extending Partially Known Networks....Pages 87-105
Integration of Genetic Variation as External Perturbation to Reverse Engineer Regulatory Networks from Gene Expression Data....Pages 107-118
Using Simulated Data to Evaluate Bayesian Network Approach for Integrating Diverse Data....Pages 119-130

✦ Subjects

Systems Biology; Bioinformatics; Biological Networks, Systems Biology; Computer Appl. in Life Sciences; Gene Expression