Ettore Rizzo

diVas interprets digenic variants using hypothesis-driven machine learning and XAI, achieving 73% sensitivity in prioritizing causative gene pairs across 11 cases.

The paper evaluates variant prioritization methods for rare disease diagnosis using data from the Rare Genomes Project, finding that top models identify causal variants in most solved families and uncover potential candidates in unsolved ones.

The paper identifies abundant viral integrations in Aedes aegypti and Ae. albopictus genomes that express piRNAs, suggesting a functional link to the piRNA pathway and antiviral defense.