Record Details

GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion

DSpace at IIT Bombay

View Archive Info
 
 
Field Value
 
Title GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion
 
Creator VINH, NX
CHETTY, M
COPPEL, R
WANGIKAR, PP
 
Description Motivation: Dynamic Bayesian networks (DBN) are widely applied in modeling various biological networks including the gene regulatory network (GRN). Due to the NP-hard nature of learning static Bayesian network structure, most methods for learning DBN also employ either local search such as hill climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing. Results: This article presents GlobalMIT, a toolbox for learning the globally optimal DBN structure from gene expression data. We propose using a recently introduced information theoretic-based scoring metric named mutual information test (MIT). With MIT, the task of learning the globally optimal DBN is efficiently achieved in polynomial time.
 
Publisher OXFORD UNIV PRESS
 
Date 2012-06-26T10:09:59Z
2012-06-26T10:09:59Z
2011
 
Type Article
 
Identifier BIOINFORMATICS,27(19)2765-2766
1367-4803
http://dx.doi.org/10.1093/bioinformatics/btr457
http://dspace.library.iitb.ac.in/jspui/handle/100/14370
 
Language English