SnorkelPlus: A Novel Approach for Identifying Relationships Among Biomedical Entities Within Abstracts

Abstract Identifying relationships between biomedical entities from unstructured biomedical text is a challenging task. SnorkelPlus has been proposed to provide the flexibility to extract these biomedical relations without any human effort. Our proposed model, SnorkelPlus, is aimed at finding connections between gene and disease entities. We achieved three objectives: (i) extract only gene and disease articles from NCBI’s, PubMed or PubMed central database, (ii) define reusable label functions and (iii) ensure label function accuracy using generative and discriminative models. We utilized deep learning methods to achieve label training data and achieved an AUROC of 85.60% for the generated gene and disease corpus from PubMed articles. Snorkel achieved an AUPR of 45.73%, which is +2.3% higher than the baseline model. We created a gene–disease relation database using SnorkelPlus from approximately 29 million scientific abstracts without involving annotated training datasets. Furthermore, we demonstrated the generalizability of our proposed application on abstracts of PubMed articles enriched with different gene and disease relations. In the future, we plan to design a graphical database using Neo4j.