iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations
The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance.
Authors
- Jialu Hou (ORCID: https://orcid.org/0000-0003-4737-7721)
- Hao Wu (ORCID: https://orcid.org/0000-0002-4138-1239)
- Alexey К. Shaytan (ORCID: https://orcid.org/0000-0003-0312-938X)
- Yumeng Liu (ORCID: https://orcid.org/0000-0002-8433-7772)
- Hang Wei (ORCID: https://orcid.org/0000-0002-0579-1716)
- Bin Liu (ORCID: https://orcid.org/0000-0003-3685-9469)
Institutions
- National Research University Higher School of Economics (RU)
- Beijing Institute of Technology (CN)
- Lomonosov Moscow State University (RU)
- International University in Moscow (RU)
- Shenzhen Technology University (CN)
Publication Details
- Journal
- BMC Biology
- Published
- 2025-05-09
- DOI
- https://doi.org/10.1186/s12915-025-02221-y
- Citations
- 4
- Primary Topic
- Cancer-related molecular mechanisms research
- Type
- article
- Field-Weighted Citation Impact
- 3.75
Funders
- National Natural Science Foundation of China