Giorgio Bini

This study uses large biobanks to identify gene-trait pairs with causal evidence, rediscover approved drug targets, and develop a predictive model for drug development.

This paper introduces catGRANULE 2.0 ROBOT, an algorithm predicting protein LLPS at single amino acid resolution, showing high performance and experimental validation.

This paper identifies low-complexity repeats as key drivers of RNA-RNA interactions and introduces RIME, a deep learning model that outperforms traditional methods in predicting these interactions.

This study compares machine learning methods for classifying multiple sclerosis and Alzheimer's disease using genomic data from UK Biobank, finding logistic regression is most robust while deep learning shows high variability.

The paper uses machine learning to analyze genotyping data and identifies interactions between SNVs in late onset Alzheimer's disease, highlighting specific genomic profiles and interactions involving APOE and other genes.