Abel González-Pérez

The paper estimates the timing of genetic driver events and clonal expansion in rhabdoid tumors using whole-genome sequencing data from 97 tumors.

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

This study identifies noncoding regulatory regions and their mutations in insulinomas, linking them to altered β-cell function and tumor development.

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 identifies a specific copy number footprint in tumors from patients exposed to platinum-based therapies using whole genome sequencing data.

The paper identifies genes driving clonal hematopoiesis by analyzing somatic mutations in blood samples from cancer cohorts, discovering novel candidates and recovering known drivers.

DeepMP detects DNA methylation from Nanopore sequencing data using a CNN that combines signals and basecalling errors, outperforming existing methods across multiple datasets.

This paper introduces BnpC, a non-parametric method for clustering single-cell mutation profiles to identify clonal populations, showing improved accuracy and scalability over existing methods.

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.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.