DMCA
Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements
BibTeX
@MISC{Dai_structurallearning,
author = {Yang Dai and Peter Larsen},
title = {Structural Learning of Genetic Regulatory Networks Based on Prior Biological Knowledge and Microarray Gene Expression Measurements},
year = {}
}
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Abstract
The reconstruction of genetic regulatory networks from microarray gene expression measurements has been a challenging problem in bioinformatics. Various methods have been proposed for this problem including the Bayesian Network (BN) approach. In this chapter we provide a comprehensive survey of the current development of using structure priors derived from highthroughput experimental results such as protein-protein interactions, transcription factor binding location data, evolutionary relationships and literature database in learning regulatory networks.
Keyphrases
genetic regulatory network microarray gene expression measurement structural learning prior biological knowledge comprehensive survey literature database evolutionary relationship highthroughput experimental result current development protein-protein interaction bayesian network various method transcription factor structure prior regulatory network location data
