Decoding epilepsy's molecular blueprint: Machine learning unravels transcriptomic subtypes and regulatory networks
These molecular subtypes and their pathways could serve as key molecular hallmarks of epilepsy, offering valuable insights for developing targeted therapies. Moreover, our findings introduce a novel framework for classifying epilepsy based on its molecular nature, potentially connecting the clinical symptoms with the underlying causes more effectively.
Authors
- Yanping Weng
- Wanwan Hou (ORCID: https://orcid.org/0000-0002-9813-4554)
- Hai Li (ORCID: https://orcid.org/0000-0002-4762-9362)
- Yangyang Ma (ORCID: https://orcid.org/0000-0002-2765-9388)
- Li Jin
- Yu An
- Yuanfeng Zhou
- Yu Ma
- Rui Zhao
- Hao Li
- Yi Wang
- Lian Chen
Institutions
- Ningbo University (CN)
- Shanghai Medical College of Fudan University (CN)
- Fudan University (CN)
- Ningbo University Affiliated Hospital (CN)
- Shanghai Cell Therapy Research Institute (CN)
- Children's Hospital of Fudan University (CN)
- Shanghai Children's Hospital (CN)
Publication Details
- Journal
- Epilepsia
- Published
- 2026-03-13
- DOI
- https://doi.org/10.1002/epi.70161
- Primary Topic
- Epilepsy research and treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00