Iván Galván‐Femenía

The paper develops MutFormer, a transformer model to predict non-coding cancer driver mutations at base-pair resolution using DNA sequence and mutational signatures.

The paper develops ORIFormer and MuSAS to predict and map human replication origins, revealing DNA sequence determinants and tissue-specific regulation through chromatin accessibility.

The paper generates a comprehensive haplotype map of the Iberian population using high-coverage whole-genome sequencing, focusing on structural variants and their imputation capabilities.

The paper validates the UK NF2 Genetic Severity Score in a Spanish cohort and proposes a revised version incorporating functional assays to improve prediction of clinical outcomes for mild and moderate phenotypes.

This study evaluates using RNA-seq data to detect kinship by estimating pairwise identity-by-descent, showing it can identify up to second-degree relationships with low to moderate sequencing depth.

This paper introduces a method combining genetic bootstrapping, multidimensional scaling, and discriminant analysis to classify genetic relationships with high accuracy using identity-by-state data.