NLM DIR Seminar Schedule
UPCOMING SEMINARS
RECENT SEMINARS
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June 30, 2026 Jaya Srivastava
Disrupted Regulation of Essential Genes Mediates Dementias and Age-Associated Disorders -
June 11, 2026 Angela Jiang
Identification and Evolutionary Analysis of Steroid-Metabolism Enzymes in Gut Microbes -
June 10, 2026 Luda Diatchenko
New Insights on Pain Biology from Human Transcriptomics: How Stimulation of Immune Response Shapes Pain Resolution -
June 9, 2026 Pascal Mutz
Characterization of covalently closed circular RNA replicators detected in (meta)transcriptomic data -
June 4, 2026 Madeleine Clore
Explaining why AlphaFold struggles to predict mutational effects
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.