Carlos G. Urzúa-Traslaviña

This study uses sequence-based deep learning models to identify functional promoter mutations in cancer, finding enrichment in known and novel cancer genes.

This paper links common and rare disease genetics by identifying key genes affected by common variants through gene regulatory networks, finding many key genes are outside GWAS loci.

The paper links genes in cancer-susceptibility loci to known somatic driver genes through tissue-specific co-expression networks derived from non-cancer samples.

This paper introduces PARM, a deep learning model trained on MPRA data to predict human promoter activity and identify TF binding preferences, revealing positional and functional differences in regulatory grammar.

The paper uses c-ICA on 34,494 bulk expression profiles to define metabolic transcriptional components and their associations with drug sensitivity, patient outcomes, and immune microenvironment.

The paper uses allele-specific expression analysis in a large cohort to identify high-impact, autosomal-dominant variants that are compensated by imbalanced expression.