Filippo Utro

This paper introduces Q-CHIPP, a quantum neural network for predicting HLA immunogenic peptides, combining MHC binding and immunogenicity prediction using quantum hardware.

The paper introduces a phylogenomics framework to infer SARS-CoV-2 sublineages and tumor clones from genomic data by analyzing variant frequencies and applying a computational method called Concerti.

The paper introduces EpiSimRA, a coalescent model with multi-way epistasis, validated by a forward Wright-Fisher model, showing superior performance in simulating evolutionary scenarios with selection and recombination.

The paper analyzes RNA sequencing data from respiratory tract samples to identify functional differences in microbiomes between COVID-19 patients, pneumonia patients, and healthy controls.