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D3GRN: a data driven dynamic network construction method to infer gene regulatory networks

Authors :
Xiang Chen
Min Li
Ruiqing Zheng
Fang-Xiang Wu
Jianxin Wang
Source :
BMC Genomics, Vol 20, Iss S13, Pp 1-8 (2019)
Publication Year :
2019
Publisher :
BMC, 2019.

Abstract

Abstract Background To infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem. Results In this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR. Conclusions We have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.

Details

Language :
English
ISSN :
14712164
Volume :
20
Issue :
S13
Database :
Directory of Open Access Journals
Journal :
BMC Genomics
Publication Type :
Academic Journal
Accession number :
edsdoj.b29208bbaa25414ba2ef192e67bebbd3
Document Type :
article
Full Text :
https://doi.org/10.1186/s12864-019-6298-5