NLM DIR Seminar Schedule
UPCOMING SEMINARS
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July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 15, 2025 Noam Rotenberg
Cell phenotypes in the biomedical literature: a systematic analysis and the NLM CellLink text mining corpus
RECENT SEMINARS
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July 3, 2025 Matthew Diller
Using Ontologies to Make Knowledge Computable -
July 1, 2025 Yoshitaka Inoue
Graph-Aware Interpretable Drug Response Prediction and LLM-Driven Multi-Agent Drug-Target Interaction Prediction -
June 10, 2025 Aleksandra Foerster
Interactions at pre-bonding distances and bond formation for open p-shell atoms: a step toward biomolecular interaction modeling using electrostatics -
June 3, 2025 MG Hirsch
Interactions among subclones and immunity controls melanoma progression -
May 29, 2025 Harutyun Sahakyan
In silico evolution of globular protein folds from random sequences
Scheduled Seminars on Feb. 8, 2022
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
Previous studies on biomedical relation extraction (RE) typically focus on extracting binary relations between two entities from a single sentence. However, complex inter-sentence relations involving multiple entity pairs, such as drug-protein and protein-disease, are commonly seen in the biomedical literature. In this talk, I will first introduce the characteristics of sentence-level RE and use the BioCreative VII DrugProt task to showcase a general text classification framework for sentence-level RE. The second part will introduce a new document-level dataset called BioRED, which covers six concept types (cell line, chemical, disease, gene, species, and variant) and eight relation pairs (e.g., chemical-disease, chemical-gene, chemical-chemical) in 600 MEDLINE abstracts. In total, BioRED consists of 20,000 entity and 6,000 relation annotations. The BioRED dataset is currently being used for developing and evaluating state-of-the-art relation extraction methods at the LitCoin natural language processing (NLP) challenge.