title: Large scale gene regulatory network inference with a multi-level strategy.
abstract: Transcriptional regulation is a basis of many crucial molecular processes and an accurate inference of the gene regulatory network is a helpful and essential task to understand cell functions and gain insights into biological processes of interest in systems biology. Inspired by the Dialogue for Reverse Engineering Assessments and Methods (DREAM) projects, many excellent gene regulatory network inference algorithms have been proposed. However, it is still a challenging problem to infer a gene regulatory network from gene expression data on a large scale. In this paper, we propose a gene regulatory network inference method based on a multi-level strategy (GENIMS), which can give results that are more accurate and robust than the state-of-the-art methods. The proposed method mainly consists of three levels, which are an original feature selection step based on guided regularized random forest, normalization of individual feature selection and the final refinement step according to the topological property of the gene regulatory network. To prove the accuracy and robustness of our method, we compare our method with the state-of-the-art methods on the DREAM4 and DREAM5 benchmark networks and the results indicate that the proposed method can significantly improve the performance of gene regulatory network inference. Additionally, we also discuss the influence of the selection of different parameters in our method.
authors.affiliation: Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai 200240, China. junwu302@gmail.com.
authors.forename: Jun
authors.initials: J
authors.lastname: Wu
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. xiaodong122@yahoo.com.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Zhao
authors.affiliation: Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904-4743, USA. zl5y@virginia.edu.
authors.forename: Zongli
authors.initials: Z
authors.lastname: Lin
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. zs9q@virginia.edu.
authors.forename: Zhifeng
authors.initials: Z
authors.lastname: Shao
mesh_terms: D000465:Algorithms; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D012044:Regression Analysis