NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Jan. 14, 2025 Ryan Bell
Comprehensive analysis of the YprA-like helicase family provides deep insight into the evolution and potential mechanisms of widespread and largely uncharacterized prokaryotic antiviral defense systems -
Jan. 16, 2025 Qingqing Zhu
GPTRadScore and CT-Bench: Advancing Multimodal AI Evaluation and Benchmarking in CT Imaging -
Jan. 17, 2025 Xuegong Zhang
Using Large Cellular Models to Understand Cell Transcriptomics Language -
Jan. 21, 2025 Qiao Jin
Artificial Intelligence for Evidence-based Medicine -
Jan. 28, 2025 Kaleb Abram
TBD
RECENT SEMINARS
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Jan. 14, 2025 Ryan Bell
Comprehensive analysis of the YprA-like helicase family provides deep insight into the evolution and potential mechanisms of widespread and largely uncharacterized prokaryotic antiviral defense systems -
Dec. 17, 2024 Joey Thole
Training set associations drive AlphaFold initial predictions of fold-switching proteins -
Dec. 10, 2024 Amr Elsawy
AI for Age-Related Macular Degeneration on Optical Coherence Tomography -
Dec. 3, 2024 Sarvesh Soni
Toward Relieving Clinician Burden by Automatically Generating Progress Notes -
Nov. 19, 2024 Benjamin Lee
Reiterative Translation in Stop-Free Circular RNAs
Scheduled Seminars on Aug. 16, 2022
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
UMLS Metathesaurus is a biomedical terminology integration system that integrates biomedical vocabularies by grouping synonymous terms into concepts. The construction and maintenance process of the UMLS Metathesaurus mainly relies on (1) the lexical and semantic processing for suggesting the groupings of synonymous terms and (2) the expertise of the UMLS editors for curating these synonymy suggestions. With the enormous knowledge accumulated over 30 years of manual curation, the existing Metathesaurus provides ample material for deep learning approaches, which have gained promising results especially in the field of natural language processing in recent years. A UMLS learning framework has been developed to explore and study various aspects of applying deep learning techniques to the vocabulary alignment task in the UMLS Metathesaurus.