Riccardo Bellazzi

Vir2vec: A Genome-Wide Viral Embedding

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

NanoCT-confocal mapping of mouse ovaries reveals lifespan-persistent symmetric organizational rules of primary-to-preovulatory follicles

The paper uses nanoCT and confocal imaging to show symmetric follicle organization in mouse ovaries across lifespan, preserved despite follicle health or atresia.

SARITA: A Large Language Model for Generating the S1 Subunit of the SARS-CoV-2 Spike Protein

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.

Digenic variant interpretation with hypothesis-driven explainable AI

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

Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders

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.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project

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.

Harnessing cytoplasmic particles movement of the human early embryo analysed by advanced imaging and artificial intelligence to predict development to blastocyst stage

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.

Autologous micrograft accelerates endogenous wound healing response via ERK-induced cell migration

This study shows that autologous micrograft therapy accelerates wound healing by enhancing fibroblast migration through ERK pathway activation and increased matrix metalloproteinase activity.