GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion
DSpace at IIT Bombay
View Archive InfoField | 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
|
|