Edoardo Giacopuzzi

Recurrent leukaemia stem cell states define the antigenic landscape of paediatric Acute Myeloid Leukaemia

This study identifies three leukaemia stem cell subtypes in paediatric AML and shows that combining CLEC12A and CD84 targets all subtypes while sparing healthy cells.

Hundreds of cardiac MRI traits derived using 3D diffusion autoencoders share a common genetic architecture

This study uses 3D diffusion autoencoders to derive latent cardiac MRI phenotypes, finds they share a genetic basis with cardiometabolic traits, and shows predictive power for disease risk.

Competing gene regulatory networks drive naive and memory B cell differentiation

This study identifies distinct gene regulatory networks driving naive and memory B cell differentiation, showing IRF4's role in both GC and plasma cell development independently of PRDM1.

An autosomal dominant cardiac arrhythmia syndrome, ST Depression Syndrome, is caused by the de novo creation of a cardiomyocyte enhancer.

The paper identifies a de novo enhancer mutation in KCNB1 causing ST Depression Syndrome, a cardiac arrhythmia, and shows it can be predicted by a deep neural network.

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types

This paper uses self-supervised learning to analyze histology images and predict spatial RNA expression across 23 human tissues, revealing population variability and its impact on gene expression.

CHARR efficiently estimates contamination from DNA sequencing data

CHARR estimates DNA contamination from variant-level sequencing data, offering accurate results with reduced computational costs compared to existing methods.

GREEN-DB: A framework for the annotation and prioritization of non-coding regulatory variants from whole-genome sequencing data

GREEN-DB annotates and prioritizes non-coding regulatory variants from whole-genome sequencing data by integrating regulatory annotations, constraint metrics, and variant impact scores.

SVRare: discovering disease-causing structural variants in the 100K Genomes Project

SVRare improves the discovery of disease-causing structural variants by aggregating and annotating rare variants from whole genome sequencing data in the 100K Genomes Project.

Investigating an in-silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression

This paper ranks antidepressants based on their similarity to gene expression profiles of treatment responders, finding escitalopram and citalopram show high rankings in some cell lines.

Genome-wide analysis of consistently RNA edited sites in human blood reveals interactions with mRNA processing genes and suggests correlations with cell types and biological variables.

The paper identifies consistently RNA-edited sites in human blood, links them to RNA processing genes, and finds correlations with cell types and biological variables.

GARFIELD-NGS: Genomic vARiants FIltering by dEep Learning moDels in NGS

GARFIELD-NGS uses deep learning to filter false positive variants in exome sequencing data, outperforming existing methods and working at low coverage.