David Martínez-Enguita

This paper introduces a PPI prior to guide autoencoder embeddings, improving biological coherence and downstream task performance in multi-omic data.

The paper develops an explainable deep learning framework to predict health scores from blood DNA methylation, showing strong predictive accuracy and alignment with biological processes.

This study uses deep autoencoders to analyze DNA methylation data and identifies disease and risk factor signatures related to aging, smoking, and disease.

The paper benchmarks disease module identification methods using GWAS and validates a multi-omic module strongly associated with multiple sclerosis risk factors.