NLM IRP Seminar Schedule
UPCOMING SEMINARS
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May 14, 2024 Stanley Liang
Knowledge-driven Latent Diffusion For COVID-19 Pneumonia Radiology Pattern Synthesis -
May 21, 2024 Ziynet Kesimoglu
TBD -
May 23, 2024 Leslie Ronish
TBD -
May 28, 2024 Harutyun Saakyan
TBD -
May 30, 2024 Deepak Gupta
TBD
RECENT SEMINARS
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May 14, 2024 Stanley Liang
Knowledge-driven Latent Diffusion For COVID-19 Pneumonia Radiology Pattern Synthesis -
May 9, 2024 Pascal Mutz
The Riboviria protein structurome expands virus protein annotation and highlights protein relations -
May 2, 2024 OPEN
TBD -
April 30, 2024 Wenya Rowe
The conformal central charge of the spin-1/2 XX model derived from long-chain asymptotics -
April 25, 2024 Ermin Hodzic
Condition-Aware Cell Type Deconvolution of Bulk Tissues
Scheduled Seminars on Feb. 29, 2024
Contact NLM_IRP_Seminar_Scheduling@mail.nih.gov with questions about this seminar.
Abstract:
Expert curation is essential to capture knowledge of enzyme functions from the scientific literature in FAIR open knowledgebases but cannot keep pace with the rate of new discoveries and new publications. In this work we present EnzChemRED, for Enzyme Chemistry Relation Extraction Dataset, a new training and benchmarking dataset to support the development of Natural Language Processing (NLP) methods such as language models that can assist enzyme curation. EnzChemRED consists of 1,210 expert curated PubMed abstracts in which enzymes and the chemical reactions they catalyze are annotated using identifiers from the UniProt Knowledgebase (UniProtKB) and the ontology of Chemical Entities of Biological Interest (ChEBI). We show that fine-tuning pre-trained language models with EnzChemRED can significantly boost their ability to identify mentions of proteins and chemicals in text (Named Entity Recognition, or NER) and to extract the chemical conversions in which they participate (Relation Extraction, or RE), with average F1 scores of 86.30% for NER, 86.66% for RE for chemical conversion pairs, and 83.79% for RE for chemical conversion pairs and linked enzymes. We combine the best performing methods after fine-tuning using EnzChemRED to create an end-to-end pipeline for knowledge extraction from text and apply this to abstracts at PubMed scale to create a draft map of enzyme functions in literature to guide curation efforts in UniProtKB and the reaction knowledgebase Rhea.