A hybrid framework for reverse engineering of robust Gene Regulatory Networks

作者：

Highlights：

• In this paper, a fast and accurate predictor set inference framework which linearly combines some inference methods is proposed.

• The purpose of the combination of various methods is to increase the accuracy of inferred GRN Please proceed.

摘要

•In this paper, a fast and accurate predictor set inference framework which linearly combines some inference methods is proposed.•The purpose of the combination of various methods is to increase the accuracy of inferred GRN Please proceed.

论文关键词：Genomics,Gene Regulatory Network (GRN),Feature selection,Information Gain (IG),ReliefF,Pearson Correlation Coefficient (PCC),Genetic Algorithm (GA)

论文评审过程：Received 12 November 2016, Revised 6 March 2017, Accepted 8 May 2017, Available online 9 June 2017, Version of Record 12 August 2017.

论文官网地址：https://doi.org/10.1016/j.artmed.2017.05.004