Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer's Disease

TL;DR

This paper presents an open-source framework for extracting disease-specific biomedical knowledge from raw text, supported by annotated datasets for Rett syndrome and Alzheimer's, and evaluates various modeling strategies including transformers.

Contribution

It introduces novel annotated datasets and benchmarks for disease-focused biomedical relation extraction, advancing methods for knowledge discovery in biomedical texts.

Findings

Transformers effectively capture semantic relations in biomedical texts.

Optimal relation and entity representations improve knowledge extraction accuracy.

Layer and attention analysis reveal how models understand biomedical relations.

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.