Assessing Artificial Intelligence (AI) Implementation for Assisting Gene Linking (at the National Library of Medicine)

作者：Rezarta Islamaj, Chih-Hsuan Wei, Po‐Ting Lai, Melanie Huston, Cathleen Coss, Preeti Gokal Kochar, Nicholas Miliaras, James G. Mork, Oleg Rodionov, Keiko Sekiya, Dorothy Trinh, Deborah Whitman, Craig Wallin, Zhiyong Lu · 发表于：JAMIA Open · 年份：2024 · DOI：10.1093/jamiaopen/ooae129 · 被引用次数：4 · 研究领域：Biomedical Text Mining and Ontologies、Bioinformatics and Genomic Networks、Genetics, Bioinformatics, and Biomedical Research

Objectives: The National Library of Medicine (NLM) currently indexes close to a million articles each year pertaining to more than 5300 medicine and life sciences journals. Of these, a significant number of articles contain critical information about the structure, genetics, and function of genes and proteins in normal and disease states. These articles are identified by the NLM curators, and a manual link is created between these articles and the corresponding gene records at the NCBI Gene database. Thus, the information is interconnected with all the NLM resources, services which bring considerable value to life sciences. National Library of Medicine aims to provide timely access to all metadata, and this necessitates that the article indexing scales to the volume of the published literature. On the other hand, although automatic information extraction methods have been shown to achieve accurate results in biomedical text mining research, it remains difficult to evaluate them on established pipelines and integrate them within the daily workflows. Materials and Methods: Here, we demonstrate how our machine learning model, GNorm2, which achieved state-of-the art performance on identifying genes and their corresponding species at the same time handling innate textual ambiguities, could be integrated with the established daily workflow at the NLM and evaluated for its performance in this new environment. Results: We worked with 8 biomedical curator experts and evaluated the int...