Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks.

ABSTRACT: The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network. However, existing methods often suffer when the number of labeled examples is low or when no negative examples are available. In this paper we propose a multi-task method that is able to simultaneously reconstruct the human and the mouse GRNs using the similarities between the two. This is done by exploiting, in a transfer learning approach, possible dependencies that may exist among them. Simultaneously, we solve the issues arising from the limited availability of examples of links by relying on a novel clustering-based approach, able to esti

SUBMITTER: Mignone P 

PROVIDER: S-EPMC7749184 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature