Enhancing Biomedical Knowledge Discovery for Diseases: An End-To-End Open-Source Framework
- URL: http://arxiv.org/abs/2407.13492v1
- Date: Thu, 18 Jul 2024 13:20:53 GMT
- Title: Enhancing Biomedical Knowledge Discovery for Diseases: An End-To-End Open-Source Framework
- Authors: Christos Theodoropoulos, Andrei Catalin Coman, James Henderson, Marie-Francine Moens,
- Abstract summary: We introduce an open-source framework designed to construct knowledge around specific diseases directly from raw text.
To facilitate research in disease-related knowledge discovery, we create two annotated datasets focused on Rett syndrome and Alzheimer's disease.
- Score: 28.68816381566995
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework designed to construct knowledge around specific diseases directly from raw text. To facilitate research in disease-related knowledge discovery, we create two annotated datasets focused on Rett syndrome and Alzheimer's disease, enabling the identification of semantic relations between biomedical entities. Extensive benchmarking explores various ways to represent relations and entity representations, offering insights into optimal modeling strategies for semantic relation detection and highlighting language models' competence in knowledge discovery. We also conduct probing experiments using different layer representations and attention scores to explore transformers' ability to capture semantic relations.
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