José Manuel Mas

The paper develops a network-guided deep learning approach to identify biomarkers for COPD-associated comorbidities using transcriptomic data and validates them in public datasets.

This study integrates prostate tissue expression datasets with machine learning to identify biomarkers that improve Gleason scoring accuracy in prostate cancer, achieving a minimum precision of 0.80.

This paper constructs a patient repository by integrating transcriptomics datasets to generate real-world data for clinical trialling, improving downstream biological conclusions by 45%.

CuBlock normalizes gene-expression microarray data across platforms, effectively separating biological groups, outperforming existing methods in this task.

The paper uses TPMS to model drug mechanisms and identify biomarkers linked to macular degeneration as an adverse effect of heart failure treatment with sacubitril/valsartan.