NLM DIR Seminar Schedule
UPCOMING SEMINARS
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Feb. 17, 2026 Zhaohui Liang
Heterogeneous Graph Re-ranking for CLIP-based Medical Cross-modal Retrieval -
Feb. 19, 2026 Jean Thierry-Mieg
On Magic2, an innovative hardware-friendly RNA-seq analyzer -
Feb. 24, 2026 Ajith Viswanathan Asari Pankajam
TBD -
March 3, 2026 Gianlucca Goncalves Nicastro
TBD -
March 5, 2026 Hasan Balci
TBD
RECENT SEMINARS
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Feb. 5, 2026 Lana Yeganova
From Algorithms to Insights: Bridging AI and Topic Discovery for Large-Scale Biomedical Literature Analysis. -
Jan. 29, 2026 Mehdi Bagheri Hamaneh
FastSpel: A simple peptide spectrum predictor that achieves deep learning-level performance at a fraction of the computational cost -
Jan. 22, 2026 Mario Flores
AI Pipeline for Characterization of the Tumor Microenvironment -
Jan. 20, 2026 Anastasia Gulyaeva
Diversity and evolution of the ribovirus class Stelpaviricetes -
Jan. 8, 2026 Won Gyu Kim
LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery
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.