Work

Structure-enhanced graph meta learning for few-shot gene regulatory network inference

Year: 2025

Type: article

Abstract: Inferring gene regulatory networks (GRNs) is essential for understanding biological regulation. Although numerous deep learning approaches have been developed for GRN inference, most require large amounts of labeled data. We present Meta-TGLink, a structure-enhanced graph meta-learning model for few-shot GRN inference. By formulating GRN inference as a link prediction task, Meta-TGLink captures transferable regulatory patterns while reducing dependence on extensive labeled datasets. The model combines graph neural networks with Transformer architectures to integrate relational and positional information, thereby improving predictive performance under data-scarce conditions. Experiments on real datasets demonstrate its superiority over state-of-the-art baselines, particularly in cross-domain few-shot scenarios.

Source: Genome biology

DOI: 10.1186/s13059-025-03860-8

Authors: Weiming Yu, Zhuobin Chen, Yaohua Hu, Jing Qin, Le Ou-Yang

Institutions: Shenzhen University, Shenzhen MSU-BIT University, Sun Yat-sen University

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $5,490

Funders: National Natural Science Foundation of China, Shenzhen Science and Technology Innovation Program, Basic and Applied Basic Research Foundation of Guangdong Province