Serial KinderMiner (SKiM) discovers and annotates biomedical knowledge using co-occurrence and transformer models.

ABSTRACT:

Background

The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The develo

SUBMITTER: Millikin RJ 

PROVIDER: S-EPMC10619245 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature