Damian Wójtowicz

The paper introduces FACTMx, a variational framework that jointly models structured and simple patient-level data to learn interpretable representations, showing improved performance in simulations and applications to cancer and cardiovascular cohorts.

The paper introduces RePrint, a method to infer shared DNA repair mechanisms between mutational signatures, outperforming existing methods and enabling guilt-by-association predictions.

The paper introduces a network-based method to uncover relationships between mutational signatures and biological processes in cancer data.

This study uses mutational signatures to assess the impact of smoking on biological processes related to SARS-CoV-2 infection and disease progression in COVID-19.

RepairSig identifies DNA repair deficiency mutational signatures in cancer by modeling interactions between mutagenic processes, replacing multiple signatures with a single representative one in breast cancer.

The paper identifies functional pathways associated with mutational signatures in breast cancer, revealing differences in the etiology of APOBEC and clock-like signatures.

NETPHIX identifies mutated subnetworks associated with drug response by leveraging network properties and integer linear programming, revealing novel gene modules linked to MEK1/2 inhibitor sensitivity.

This paper introduces sequence-dependent models for mutation signatures, improving the resolution of mutation signature activity in cancer by accounting for dependencies between mutations.

The paper assesses confidence and stability in decomposing mutational signatures in breast cancer genomes and identifies stable, unstable, and previously missed signatures.