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"item_title" : "Gene Network Inference",
"item_author" : [" Alberto Fuente "],
"item_description" : "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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Gene Network Inference : Verification of Methods for Systems Genetics Data
Overview
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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Details
- ISBN-13: 9783662522042
- ISBN-10: 3662522047
- Publisher: Springer
- Publish Date: August 2016
- Dimensions: 9.21 x 6.14 x 0.31 inches
- Shipping Weight: 0.47 pounds
- Page Count: 130
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