Judith Farrés

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.