Francesco Gualdi

The paper introduces a pipeline to create curated, balanced variant-trait association datasets for complex disorders using GWAS and functional annotations.

The paper introduces C2BM, a deep learning model that improves SNP prioritization by integrating genomic annotations and trait relationships, showing better predictions when related trait labels are available.

The paper develops two novel knowledge graph embedding algorithms, DLemb and BioKG2Vec, which outperform existing methods in predicting gene-disease associations, particularly for diseases with limited information.

Genopyc is a Python library for analyzing complex diseases' genetic basis, enabling integration and visualization of genomic data to uncover dysregulated pathways.