A novel mutual information-based Boolean network inference method fromtime-series gene expression data

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/473849.do
DOI
10.1371/journal.pone.0171097
Title
A novel mutual information-based Boolean network inference method fromtime-series gene expression data
Description
This research was supported by the Basic Science Research Programthrough the National Research Foundation of Korea funded by the Ministryof Education (2015R1D1A1A09060910) [http://www.nrf.re.kr/nrf.eng_crns/].The funder had no role in study design, data collection and analysis,decision to publish, or preparation of the manuscript.
abstract
Background
Inferring a gene regulatory network from time-series gene expression data in systems biology is a challenging problem. Many methods have been suggested, most of which have a scalability limitation due to the combinatorial cost of searching a regulatory set of genes. In addition, they have focused on the accurate inference of a network structure only. Therefore, there is a pressing need to develop a network inference method to search regulatory genes efficiently and to predict the network dynamics accurately.
Results
In this study, we employed a Boolean network model with a restricted update rule scheme to capture coarse-grained dynamics, and propose a novel mutual information-based Boolean network inference (MIBNI) method. Given time-series gene expression data as an input, the method first identifies a set of initial regulatory genes using mutual information-based feature selection, and then improves the dynamics prediction accuracy by iteratively swapping a pair of genes between sets of the selected regulatory genes and the other genes. Through extensive simulations with artificial datasets, MIBNI showed consistently better performance than six well-known existing methods, REVEAL, Best-Fit, RelNet, CST, CLR, and BIBN in terms of both structural and dynamics prediction accuracy. We further tested the proposed method with two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network, and also observed better results using MIBNI compared to the six other methods.
Conclusions
Taken together, MIBNI is a promising tool for predicting both the structure and the dynamics of a gene regulatory network.
provenance
Made available in Cube on 2018-09-28T10:30:29Z (GMT). No. of bitstreams: 0
language
English
author
Barman, Shohag
Kwon, Yung-Keun
accessioned
2018-09-28T10:30:29Z
available
2018-09-28T10:30:29Z
issued
2017
citation
PLOS ONE(12): 2
issn
1932-6203
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/473849.do
Funder
교육부
Funding Program
BK21플러스사업(0.5)
Project ID
1345274006
Jurisdiction
Rep.of Korea
Project Name
Automobile/Ship Electronics Convergence Center
rights
openAccess
type
article


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