Identifying indications for novel drugs using electronic health records
Finding new indications for drugs is extremely valuable. Our results explore how to best use EHR data for finding new indications for first in class drugs drug using a phenotypical-similarity driven approach. Our methods can be integrated with others methods using multiple data modalities such as chemical, molecular, genetic data.
Authors
- Chaorui Zhang (ORCID: https://orcid.org/0000-0002-1530-2584)
- L. Adámek (ORCID: https://orcid.org/0000-0001-6005-2812)
- Brandon Rufino (ORCID: https://orcid.org/0000-0003-0253-7496)
- Flavio Dormont (ORCID: https://orcid.org/0000-0001-7309-1853)
- Ramón Díaz Hernández (ORCID: https://orcid.org/0000-0003-1338-9046)
- Ziv Bar‐Joseph (ORCID: https://orcid.org/0000-0003-3430-6051)
- Nicolas Capit
- Ingrid O'Dwyer
- Greg Padiasek
Institutions
- Sanofi (France) (FR)
- Sanofi (Canada) (CA)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-10-23
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109158
- Citations
- 1
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.23
Funders
- Sanofi