Gennaro Gambardella

ESMRank is a sequence-based predictor that integrates protein language models and physicochemical features to rank mutational constraint across proteins, outperforming existing methods and capturing disease-relevant variants.

This study combines long- and short-read sequencing data using a hybrid DeepVariant model to improve germline variant detection accuracy.

This paper introduces two methods, scGSEA and scMAP, to improve functional annotation of scRNA-seq data by using gene set enrichment and transfer learning.

The paper creates a breast cancer cell line atlas to study tumour heterogeneity and drug response by integrating single-cell data with bulk gene expression and drug screening results.