Gene Network Inference: Verification of Methods for Systems Genetics Data
Author:   Alberto Fuente
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:  

9783642451607


Pages:   130
Publication Date:   15 January 2014
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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Gene Network Inference: Verification of Methods for Systems Genetics Data


Overview

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.

Full Product Details

Author:   Alberto Fuente
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Dimensions:   Width: 15.50cm , Height: 1.00cm , Length: 23.50cm
Weight:   3.435kg
ISBN:  

9783642451607


ISBN 10:   3642451608
Pages:   130
Publication Date:   15 January 2014
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Simulation of the Benchmark Datasets.- A Panel of Learning Methods for the Reconstruction of Gene Regulatory Networks in a Systems Genetics Context.- Benchmarking a simple yet effective approach for inferring gene regulatory networks from systems genetics data.- Differential Equation based reverse-engineering algorithms: pros and cons.- Gene regulatory network inference from systems genetics data using tree-based methods.- Extending partially known networks.- Integration of genetic variation as external perturbation to reverse engineer regulatory networks from gene expression data.- Using Simulated Data to Evaluate Bayesian Network Approach for Integrating Diverse Data.

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