Ivanisenko, T.V.; Demenkov, P.S.; Kolchanov, N.A.; Ivanisenko, V.A. The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition. Int. J. Mol. Sci.2022, 23, 14934.
Ivanisenko, T.V.; Demenkov, P.S.; Kolchanov, N.A.; Ivanisenko, V.A. The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition. Int. J. Mol. Sci. 2022, 23, 14934.
Ivanisenko, T.V.; Demenkov, P.S.; Kolchanov, N.A.; Ivanisenko, V.A. The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition. Int. J. Mol. Sci.2022, 23, 14934.
Ivanisenko, T.V.; Demenkov, P.S.; Kolchanov, N.A.; Ivanisenko, V.A. The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition. Int. J. Mol. Sci. 2022, 23, 14934.
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%. The new ANDDigest version (01.2022) has a web interface and is freely available to users at https://anddigest.sysbio.ru/.
Keywords
Text-mining; ANDDigest; ANDSystem; Named entity recognition; Machine learning; PubMedBERT
Subject
Social Sciences, Library and Information Sciences
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
