Ferran Muiños

Oncodrive3D detects structural clusters of somatic mutations under positive selection in the human proteome using AlphaFold 2 models, showing high sensitivity and computational efficiency.

The paper develops machine learning models to identify clonal hematopoiesis driver mutations, outperforming existing methods and applying them to UK Biobank data to confirm known associations.

This paper identifies mutational processes with high hotspot propensity in tumors and normal tissues, showing that SBS1 and SBS17a/b have distinct genomic factors influencing their hotspot formation.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

This study identifies mutational footprints of six cancer therapies using whole-genome data from metastatic tumors to assess their contribution to mutation burden and risk of driver mutations.

OncodriveCLUSTL identifies cancer drivers by detecting mutation clustering signals in genomic data, outperforming existing methods across tumor whole-exomes.

The paper identifies degrons and E3 ligase alterations across cancer types, finding that at least 1 in 7 driver mutations affect these components.

This study identifies genomic and transcriptomic traits associated with immune-phenotypes in 9,403 solid tumors across 29 cancers, revealing patterns linked to immune evasion and tumor progression.

This paper identifies driver mutations across 2,500 tumors from 37 cancer types, revealing a whole-genome panorama of genomic events that underlie tumor development.