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Gene Network Inference: Verification of Methods for Systems Genetics Data

Editat de Alberto Fuente
en Limba Engleză Hardback – 14 ian 2014
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.
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Specificații

ISBN-13: 9783642451607
ISBN-10: 3642451608
Pagini: 144
Ilustrații: XI, 130 p. 49 illus., 33 illus. in color.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.38 kg
Ediția:2013
Editura: Springer Berlin, Heidelberg
Colecția Springer
Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

Describes and evaluates recent methods for System Genetics data analysis
Critically evaluates various algorithms used to analyze Systems Genetics data
Put together in a community effort by the experts in the field
Includes supplementary material: sn.pub/extras