GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data
The experimental results and case studies illustrate the considerable performance of GRLGRN in predicting gene interactions and provide interpretability for the prediction tasks, such as identifying hub genes in the network and uncovering implicit links.
Authors
- Jingwen Zhou (ORCID: https://orcid.org/0000-0002-3949-3733)
- Xiaoli Luan (ORCID: https://orcid.org/0000-0002-4805-1726)
- Kai Wang (ORCID: https://orcid.org/0000-0003-4802-520X)
- Yulong Li (ORCID: https://orcid.org/0000-0002-1852-9469)
- Xinglong Wang
- Fei Liu (ORCID: https://orcid.org/0000-0003-1734-7214)
Institutions
- Jiangnan University (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1186/s12859-025-06116-1
- Citations
- 3
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 2.42
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Government of Jiangsu Province
- Natural Science Foundation of Jiangsu Province