Sara Capponi

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 uses machine learning on molecular dynamics simulations to identify key residues determining Wnt-Wls binding specificity across four Wnt proteins.

This paper introduces a high-throughput method for quantifying Drosophila fecundity using automated imaging and transfer systems, achieving high accuracy and efficiency in fecundity assays.

The paper uses synthetic costimulatory domains and machine learning to identify design rules for CARs that enhance cytotoxicity and stemness.

This study develops a deep learning model to predict phenotype probability distributions from noisy biological data with few observations per condition.

The paper predicts epitope candidates for SARS-CoV-2 by analyzing epitope sequences from related coronaviruses and identifying conserved and diverse antigen regions across 62k genomes.

This paper develops a framework combining CNN and unsupervised learning to map genotype to vacuole morphology in yeast, identifying 4 phenotypic groups with 85% purity and 80% prediction accuracy.