LiPLike: towards gene regulatory network predictions of high certainty.

ABSTRACT: MOTIVATION:High correlation in expression between regulatory elements is a persistent obstacle for the reverse-engineering of gene regulatory networks. If two potential regulators have matching expression patterns, it becomes challenging to differentiate between them, thus increasing the risk of false positive identifications. RESULTS:To allow for gene regulation predictions of high confidence, we propose a novel method, the Linear Profile Likelihood (LiPLike), that assumes a regression model and iteratively searches for interactions that cannot be replaced by a linear combination of other predictors. To compare the performance of LiPLike with other available inference methods, we benchmarked LiPLike using three independent datasets from the Dialogue on Reverse Engineering Assessment and M

SUBMITTER: Magnusson R 

PROVIDER: S-EPMC7178405 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature