Full list of potential drug repurposing candidates for rare neuro-muscular disorders

This work presents a holistic machine learning approach for drug-disease link prediction, leveraging multiple heterogeneous sources including biomedical literature, structured databases, and textual descriptions of diseases. Focusing on seven rare neuro-muscular disorders, we construct a biomedical knowledge graph from literature and open databases, to evaluate a suite of rule-based, graph neural network, and path-encoding models.

An ensemble of the best-performing methods, further enriched with disease similarity features derived from text-based embeddings, is used to generate candidate treatments for each disorder. Experimental results show that established graph neural network approaches (CompGCN), and path encoding methods (Prime Adjacency Matrix framework), outperform other approaches in metrics like Mean Reciprocal Rank. The ensemble of the best-performing methods further improves those metrics, reaching MRR = 0.3145.