1 Computer Science and Software Engineering, Concordia University, 1455 Boulevard de Maisonneuve Ouest, Montreal, Quebec H3G1M8, Canada.

BENIN: Biologically enhanced network inference.

J Bioinform Comput Biol. 2020 Jun;18(3):2040007

Authors: Wonkap SK, Butler G

Abstract

Gene regulatory network inference is one of the central problems in computational biology. We need models that integrate the variety of data available in order to use their complementarity information to overcome the issues of noisy and limited data. BENIN: Biologically Enhanced Network INference is our proposal to integrate data and infer more accurate networks. BENIN is a general framework that jointly considers different types of prior knowledge with expression datasets to improve the network inference. The method states the network inference as a feature selection problem and uses a popular penalized regression method, the Elastic net, combined with bootstrap resampling to solve it. BENIN significantly outperforms the state-of-the-art methods on the simulated data from the DREAM 4 challenge when combining genome-wide location data, knockout gene expression data, and time series expression data.

PMID: 32698722 [PubMed - in process]