AI Article Synopsis
- The volume of scientific literature is increasing rapidly, with 1.5 million biomedical abstracts added to PubMed in 2021, highlighting the need for advanced information retrieval systems.
- The authors developed an improved version of ANDDigest, a web-based system that utilizes specialized ontologies and artificial intelligence to effectively search PubMed.
- This new version employs PubMedBERT classifiers, boosting the accuracy of recognizing short names for molecular genetics entities by 13% across eight biological categories.
Article Abstract
The body of scientific literature continues to grow annually. Over 1.5 million abstracts of biomedical publications were added to the PubMed database in 2021. Therefore, developing cognitive systems that provide a specialized search for information in scientific publications based on subject area ontology and modern artificial intelligence methods is urgently needed. We previously developed a web-based information retrieval system, ANDDigest, designed to search and analyze information in the PubMed database using a customized domain ontology. This paper presents an improved ANDDigest version that uses fine-tuned PubMedBERT classifiers to enhance the quality of short name recognition for molecular-genetics entities in PubMed abstracts on eight biological object types: cell components, diseases, side effects, genes, proteins, pathways, drugs, and metabolites. This approach increased average short name recognition accuracy by 13%.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738852 | PMC |
| http://dx.doi.org/10.3390/ijms232314934 | DOI Listing |
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