Adalberto Claudio Quiros

This study identifies architectural subtypes of PDAC using morphospatial profiling of CAFs, showing improved prognostication and chemotherapy response prediction.

This paper uses self-supervised AI to identify under-appreciated stromal morphologies in lung adenocarcinoma linked to poor prognosis, independent of current clinical schemas.

This paper uses self-supervised learning on 3446 H&E-stained mesothelioma slides to create a histomorphological atlas, achieving high predictive performance and revealing molecular insights.

This paper uses self-supervised learning to identify histomorphological patterns in colon cancer WSIs, revealing clinically relevant clusters associated with survival and treatment responses.

PLM-interact improves PPI prediction by jointly encoding protein pairs, achieving 16-28% AUPR gains across multiple species.