GCLink: a graph contrastive link prediction framework for gene regulatory network inference.

ABSTRACT:

Motivation

Gene regulatory networks (GRNs) unveil the intricate interactions among genes, pivotal in elucidating the complex biological processes within cells. The advent of single-cell RNA-sequencing (scRNA-seq) enables the inference of GRNs at single-cell resolution. However, the majority of current supervised network inference methods typically concentrate on predicting pairwise gene regulatory interaction, thus failing to fully exploit correlations among all genes and exhibiting limited generalization performance.

Results

To address these issues, we propose a graph contrastive link prediction (GCLink) model to infer potential gene regulatory interactions from scRNA-seq data. Based on known gene regulatory interactions and scRNA-seq data, GCLink introduces a graph contras

SUBMITTER: Yu W 

PROVIDER: S-EPMC11881698 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature