Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer's Disease
This addresses the need for efficient knowledge discovery in biomedical research, particularly for diseases like Rett syndrome and Alzheimer's, but is incremental as it builds on existing methods for relation detection.
The authors tackled the problem of efficiently discovering knowledge from biomedical publications by introducing an open-source framework to construct disease-specific knowledge from raw text, applied to Rett syndrome and Alzheimer's disease with annotated datasets and benchmarking that provided insights into optimal modeling strategies for semantic relation detection.
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.