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Gene Network Inference : Verification of Methods for Systems Genetics Data / edited by Alberto Fuente.
- Format:
- Book
- Series:
- Biomedical and Life Sciences (Springer-11642)
- Language:
- English
- Subjects (All):
- Systems biology.
- Bioinformatics.
- Biological systems.
- Computational biology.
- Gene expression.
- Systems Biology.
- Computer Appl. in Life Sciences.
- Gene Expression.
- Local Subjects:
- Systems Biology.
- Bioinformatics.
- Computer Appl. in Life Sciences.
- Gene Expression.
- Physical Description:
- 1 online resource (XI, 130 pages) : 49 illustrations, 33 illustrations in color
- Edition:
- First edition 2013.
- Contained In:
- Springer eBooks
- Place of Publication:
- Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
- System Details:
- text file PDF
- Summary:
- 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.
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-642-45161-4
- 9783642451614
- Access Restriction:
- Restricted for use by site license.
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