Giovanna Nicora

Vir2vec is a genome-wide viral embedding model that outperforms existing models in viral genome understanding tasks and provides explainability for host adaptation mutations.

SARITA is a large language model that generates synthetic SARS-CoV-2 Spike S1 sequences, accurately predicting mutations in future variants like Delta and Omicron.

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

This paper introduces DeepAutoCoV, a deep learning system that predicts future dominant SARS-CoV-2 lineages by detecting anomalies in genomic data, achieving better performance than baseline methods with early detection of emerging variants.

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.

This paper uses AI and imaging to analyze cytoplasmic movement in early human embryos to predict blastocyst development, showing improved accuracy when combining AI with expert assessment.