Matteo Cereda

KDM provides a unified framework for motif discovery, analysis, and binding prediction by embedding DNA/RNA sequences and motifs in a shared k-mer space using the Hellinger transformation and Bhattacharyya coefficient.

RNAMaRs infers RNA motifs and their regulatory proteins by integrating motif discovery, binding data, and splicing responses, showing consistent prioritization of perturbed regulators in ENCODE datasets.

Kandinsky identifies and analyzes cell neighbourhoods in spatial omics data to reveal functional interactions across biological contexts.

This study identifies patient-specific cancer genes in esophageal adenocarcinoma using machine learning, revealing recurrent process perturbations that suggest therapeutic targets.