Double optimal transport for differential gene regulatory network inference with unpaired samples.

ABSTRACT:

Motivation

Inferring differential gene regulatory networks (GRNs) between different conditions from gene expression profiles remains a significant challenge. Current GRN inference approaches are limited by either scalability in large networks or accuracy in high-dimensional scenarios. Furthermore, most existing methods require paired samples for comparative GRN analyses.

Results

To overcome these challenges, we model gene regulation as a distribution transportation problem and propose an efficient and effective method, called double optimal transport (OT), for reconstructing differential GRNs from the perspective of optimal transport theory, applicable to unpaired samples. Double OT is a novel two-level OT framework. It first aligns unpaired samples by solving a partial OT p

SUBMITTER: Li M 

PROVIDER: S-EPMC12342166 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature