Fabio Socciarelli

This study shows that inhibiting de novo lipogenesis and supplementing with PUFAs suppresses prostate cancer progression by inducing oxidative stress and cell death.

This study uses spatial imaging to identify architectural changes in bone marrow that predict disease activity and response to therapy in myelodysplastic neoplasms.

This paper introduces MIMA, a deep learning framework for multi-omics integration and batch correction, which effectively removes batch effects and preserves biological information across modalities.

The paper introduces statistical tools to analyze immune cluster patterns in prostate cancer and finds high-grade tumors show more immune infiltration and active immune features.

SAHA creates a multimodal, subcellular-resolution reference atlas of healthy human tissues, revealing spatial organization and disease-associated disruptions across multiple organ systems.

KODAMA improves spatial transcriptomics analysis by reducing dimensionality while preserving spatial information, showing high accuracy across multiple datasets.

This paper integrates multiple spatial-omics modalities in prostate cancer samples to reveal molecular heterogeneity and correlations between lipids and gene expression.