BADGER: biologically-aware interpretable differential gene expression ranking model
This makes it particularly valuable for drug repurposing scenarios, especially in developing therapeutic plans for new or resistant diseases by leveraging similarities with other diseases. All code and data used in this study are available at https://github.com/dmis-lab/BADGER.git.
Authors
- Sunkyu Kim (ORCID: https://orcid.org/0000-0002-0240-6210)
- Mogan Gim (ORCID: https://orcid.org/0000-0002-6458-7723)
- Hwihyun Kim (ORCID: https://orcid.org/0000-0003-4770-688X)
- Jaewoo Kang (ORCID: https://orcid.org/0000-0001-6798-9106)
- Soyon Park
- Seungheun Baek (ORCID: https://orcid.org/0000-0002-3231-478X)
Institutions
- University of Seoul (KR)
- American International Group (United States) (US)
- Korea University (KR)
- Hankuk University of Foreign Studies (KR)
Publication Details
- Journal
- Bioinformatics Advances
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/bioadv/vbaf029
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Research Foundation
- Hankuk University of Foreign Studies
- Korea Health Industry Development Institute
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion