Lucia Trastulla

This paper develops GepiClock, an epigenetic clock for cancer cells, revealing age-related therapeutic vulnerabilities in cancer cell lines.

The paper trains an ensemble classifier to predict cetuximab sensitivity in colorectal cancer PDXs using multi-omic data, achieving high accuracy and demonstrating its utility for therapeutic biomarker development.

This study benchmarks seven computational methods for correcting CN and proximity biases in CRISPR-Cas9 screens, finding AC-Chronos and CRISPRcleanR perform best in different scenarios.

The paper identifies genetically distinct patient subgroups across diseases by analyzing polygenic risk and pathway convergence.

This study identifies alternative polyadenylation (APA) as a molecular mechanism linking polygenic risk for schizophrenia to synaptic impairments through functional analysis in induced neurons.

The paper functionally annotates schizophrenia-associated non-coding variants by identifying cell type and condition specific molecular functions and target genes.

The paper develops a computational framework to create reduced gene templates for supervised analysis of scale-limited CRISPR-Cas9 screens, improving efficiency without compromising results.

This study identifies temporally divergent regulatory mechanisms in neuronal development and maturation across mouse and marmoset neocortex.

The paper introduces INF, a multi-omics framework that improves cancer classification by integrating gene expression, protein, and copy number data, achieving better accuracy and smaller feature sets than single-omics models.