Xiang Chen1, Min Li2, Ruiqing Zheng1, Fang-Xiang Wu3, Jianxin Wang1. 1. School of Computer Science and Engineering, Central South University, Changsha, China. 2. School of Computer Science and Engineering, Central South University, Changsha, China. limin@mail.csu.edu.cn. 3. Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
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.
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.
Authors: Daniel Marbach; Robert J Prill; Thomas Schaffter; Claudio Mattiussi; Dario Floreano; Gustavo Stolovitzky Journal: Proc Natl Acad Sci U S A Date: 2010-03-22 Impact factor: 11.205
Authors: Tong Ihn Lee; Nicola J Rinaldi; François Robert; Duncan T Odom; Ziv Bar-Joseph; Georg K Gerber; Nancy M Hannett; Christopher T Harbison; Craig M Thompson; Itamar Simon; Julia Zeitlinger; Ezra G Jennings; Heather L Murray; D Benjamin Gordon; Bing Ren; John J Wyrick; Jean-Bosco Tagne; Thomas L Volkert; Ernest Fraenkel; David K Gifford; Richard A Young Journal: Science Date: 2002-10-25 Impact factor: 47.728