Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.

ABSTRACT: Accurate inference of gene regulatory networks (GRNs) is important to unravel unknown regulatory mechanisms and processes, which can lead to the identification of treatment targets for genetic diseases. A variety of GRN inference methods have been proposed that, under suitable data conditions, perform well in benchmarks that consider the entire spectrum of false-positives and -negatives. However, it is very challenging to predict which single network sparsity gives the most accurate GRN. Lacking criteria for sparsity selection, a simplistic solution is to pick the GRN that has a certain number of links per gene, which is guessed to be reasonable. However, this does not guarantee finding the GRN that has the correct sparsity or is the most accurate one. In this study, we provide a general a

SUBMITTER: Secilmis D 

PROVIDER: S-EPMC9340570 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature