Abstract

There is a high potential for data leakage in biomedical machine learning applications since biomedical data resources share, reuse and import data from each other routinely. We have investigated potential data leakage in the prediction of protein-protein interactions using the Gene Ontology knowledge graph, by comparing the performance of models trained and tested on the same versions of data versus training on archived data and predicting only for newly discovered protein interactions. Our results were not able to detect an influence of data leakage, indicating that if this problem exists, its magnitude is not affecting the performance of knowledge graph-based protein interaction predictions.