MeSH2Matrix: combining MeSH keywords and machine learning for biomedical relation classification based on PubMed.

ABSTRACT: Biomedical relation classification has been significantly improved by the application of advanced machine learning techniques on the raw texts of scholarly publications. Despite this improvement, the reliance on large chunks of raw text makes these algorithms suffer in terms of generalization, precision, and reliability. The use of the distinctive characteristics of bibliographic metadata can prove effective in achieving better performance for this challenging task. In this research paper, we introduce an approach for biomedical relation classification using the qualifiers of co-occurring Medical Subject Headings (MeSH). First of all, we introduce MeSH2Matrix, our dataset consisting of 46,469 biomedical relations curated from PubMed publications using our approach. Our dataset includes a m

SUBMITTER: Turki H 

PROVIDER: S-EPMC11445994 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature