TY - JOUR
T1 - Self-Supervised Graph Representation Learning for Predicting Drug Repurposing Candidates for Rare Pediatric Cancers Using Protein-Protein Interaction Networks and Gene Expression Data
AU - Luis Fernandez
AU - Marta Ruiz
AU - Sergio Navarro
JF - Journal of Artificial Intelligence for Healthcare Systems
JO - J. Artif. Intell. Healthc. Syst.
SN - 3149-8981
Y1 - 2025
VL - 4
IS - 2
SP - 112
N2 - Rare pediatric cancers are difficult to treat due to their very low incidence, which limits drug development and makes experimental screening of therapies slow, costly, and dependent on scarce tumor samples. Traditional supervised machine learning approaches are also constrained by the lack of labeled drug–response data, while rich but unlabeled protein–protein interaction networks remain underutilized. We propose a self-supervised graph representation learning framework that integrates protein interaction networks with patient gene expression data to support drug repurposing. The model builds a heterogeneous graph of drugs, genes, diseases, and proteins, and uses a graph neural network trained with self-supervised objectives such as contrastive learning and masked prediction to learn molecular representations without labeled data. It is then fine-tuned on small pediatric cancer datasets. The framework enables prediction of candidate drug therapies by combining learned biological network representations with disease-specific expression profiles. This approach reduces reliance on large labeled datasets and allows adaptation to rare cancer contexts, offering a scalable strategy for computational drug repurposing in pediatric oncology.
UR - https://cirpublications.com/z662784540
ER -