Your browser doesn't support javascript.
The New Version of the ANDDigest Tool with Improved AI-Based Short Names Recognition.
Ivanisenko, Timofey V; Demenkov, Pavel S; Kolchanov, Nikolay A; Ivanisenko, Vladimir A.
  • Ivanisenko TV; Kurchatov Genomics Center, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk 630090, Russia.
  • Demenkov PS; Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk 630090, Russia.
  • Kolchanov NA; Kurchatov Genomics Center, Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk 630090, Russia.
  • Ivanisenko VA; Institute of Cytology & Genetics, Siberian Branch, Russian Academy of Sciences, Prospekt Lavrentyeva 10, Novosibirsk 630090, Russia.
Int J Mol Sci ; 23(23)2022 Nov 29.
Article in English | MEDLINE | ID: covidwho-2296973
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%.
Subject(s)
Keywords
Fulltext
Print
XML
PubMed Links
Search on Google

Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Data Mining Type of study: Reviews Language: English Year: 2022 Document Type: Article Affiliation country: Ijms232314934

Similar

MEDLINE
LILACS
LIS
Fulltext
Print
XML
PubMed Links
Search on Google

Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Data Mining Type of study: Reviews Language: English Year: 2022 Document Type: Article Affiliation country: Ijms232314934