Davide Cirillo

This paper uses generative and symbolic AI to forecast kidney cancer progression from cross-sectional data, providing interpretable models and molecular insights.

The paper identifies sex-specific transcriptomic similarities between comorbid diseases and finds that 13-16% of disease pairs show sex-specific transcriptome similarity, recapitulating 53-60% of known sex-specific comorbidities.

The paper identifies a conserved set of transcription factors in human CD4+ T cell subtypes and shows subtype-specific rewiring of gene regulatory networks during activation and differentiation.

The paper disentangles genetic and non-genetic factors contributing to disease co-occurrences using RNA-seq, heritability data, and UK Biobank genomic data.

This paper reviews the use of deep representation learning, particularly VAEs, in cancer omics to model temporal processes, highlighting limitations in capturing cancer progression dynamics.

This study uses synthetic data generation and explainable AI to uncover an intermediate subgroup in medulloblastoma, highlighting treatment disparities and supporting the continuum hypothesis between Groups 3 and 4.

This paper uses multilayer network analysis to identify molecular determinants of severity in Congenital Myasthenic Syndromes by integrating protein-protein interactions, pathways, and metabolomics data.

The paper develops a graph-based framework to infer direct associations between HPO and GO terms, improving the interpretation of molecular processes in disease contexts.