Prediction of gene regulatory connections with joint single-cell foundation models and graph-based learning.

ABSTRACT:

Motivation

Single-cell RNA sequencing (scRNA-seq) data offers unprecedented opportunities to infer gene regulatory networks (GRNs) at a fine-grained resolution, shedding light on cellular phenotypes at the molecular level. However, the high sparsity, noise, and dropout events inherent in scRNA-seq data pose significant challenges for accurate and reliable GRN inference. The rapid growth in experimentally validated transcription factor-DNA binding data has enabled supervised machine learning methods, which rely on known regulatory interactions to learn patterns, and achieve high accuracy in GRN inference by framing it as a gene regulatory link prediction task. This study addresses the gene regulatory link prediction problem by learning vectorized representations at the gene level to

SUBMITTER: Kommu S 

PROVIDER: S-EPMC12261397 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature