This paper is in the following e-collection/theme issue:

Natural Language Processing (945) Adverse Drug Events Detection, Pharmacovigilance and Surveillance (225) Machine Learning (2211) Generative Language Models Including ChatGPT (790)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67513, first published .
Predicting Drug–Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach

Predicting Drug–Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach

Predicting Drug–Side Effect Relationships From Parametric Knowledge Embedded in Biomedical BERT Models: Methodological Study With a Natural Language Processing Approach

Authors of this article:

Woohyuk Jeon1 Author Orcid Image ;   Minjae Park1 Author Orcid Image ;   Doyeon An1 Author Orcid Image ;   Wonshik Nam1 Author Orcid Image ;   Ju-Young Shin2 Author Orcid Image ;   Seunghee Lee3 Author Orcid Image ;   Suehyun Lee1 Author Orcid Image
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Journals

  1. Nandi S, Banerjee S, Manna D, Awasthi A, Biswas A, Das A, Mukherjee B, Mitra P, Mandal M. Targeting of HSP27 and MMP-2/9 Crosstalk by High-Throughput Drug Repurposing Strategies Identifies Paroxetine as a Potential Candidate in Glioblastoma. Journal of Medicinal Chemistry 2026;69(4):4659 View