NLM IRP Seminar Schedule
UPCOMING SEMINARS
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May 7, 2024 OPEN
TBD -
May 9, 2024 Pascal Mutz
The Riboviria protein structurome expands virus protein annotation and highlights protein relations -
May 14, 2024 Stanley Liang
TBD -
May 16, 2024 Diego Salazar
TBD -
May 21, 2024 Ziynet Kesimoglu
TBD
RECENT SEMINARS
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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 -
April 16, 2024 Jaya Srivastava
Regulatory plasticity of the human genome -
April 11, 2024 Sergey Shmakov
Comprehensive survey of the TnpB RNA-guided nucleases
Scheduled Seminars on March 3, 2022
Contact NLM_IRP_Seminar_Scheduling@mail.nih.gov with questions about this seminar.
Abstract:
Biomedical relation extraction (RE) aims to develop computational methods to extract the associations between biomedical entities from unstructured texts automatically. This task is crucial in various biomedical research topics such as biological knowledge/drug discovery. Most existing RE approaches formulate this task as a classification problem to categorize the entity pairs with relation or not. This type of methods is required to process all the pairs between two entities one by one, which is very time-consuming and not able to handle large-scale data using advanced deep learning techniques. Moreover, these methods ignore the dependency between multiple relations since they deconstructed RE into multiple independent relation classification subtasks. To address these problems, we propose a novel sequence labeling framework for the biomedical RE task. Our proposed framework has been evaluated on two independent applications. 1) Drug-protein interaction extraction, 2) Recognizing the corresponding species of gene names in the literature. Taken together, our proposed framework is more efficient and is able to fully exploit the dependencies of relations for improved performance on biomedical RE tasks.