Stephanie Portelli

This study uses Methylscape to track breast cancer progression by detecting epigenetic changes during EMT and developing a low-cost platform for monitoring.

This paper develops UshEffect-3D, a variant effect predictor for USH2A missense variants using sequence and predicted structure features, reclassifying 37.2% of ClinVar VUS as likely pathogenic.

CharacTERT classifies hTERT missense variants using machine learning models that integrate sequence and structure-based features, achieving high accuracy and sensitivity in predicting pathogenicity.

The paper introduces AMRscope, a protein language model-based tool for predicting antimicrobial resistance likelihood from single mutations, showing strong performance across bacterial species and identifying new resistance candidates.

The paper presents a dashboard for comparing and visualizing pathogenicity predictions of missense mutations in the human proteome within a 3D structural context.

The paper presents a web resource, COVID-3D, that visualizes SARS-CoV-2 genetic variation in 3D structures to aid in understanding its impact on infection, immunity, and drug development.