Jesús S. Mora Pardina

This paper develops a deep learning model using SNP microarray data to predict ALS risk with high accuracy across diverse populations and genotyping platforms.

The paper investigates oligogenic risk in ALS using genome sequencing data, finding that multiple rare variants in known ALS genes increase disease risk but not clinical outcomes.

The paper introduces a method to compress genetic data using autoencoders that leverage linkage disequilibrium structure, achieving high reconstruction accuracy while reducing data size.

This study analyzes genetic and clinical data from PLS patients to assess the role of genetic testing and identify potential disease-associated variants.

The paper analyzes CAV1 and CAV2 expression in ALS brain tissue and finds higher expression in ALS patients, associated with increased survival and slower disease progression.

This study identifies NEFH tail domain mutations as risk factors for ALS through meta-analysis and variant burden analysis in case-control studies and whole-genome sequencing data.

This study identifies 690 candidate ALS genes using a hierarchical Bayesian model, highlighting genetic convergence on the distal axon and functional significance in motor neurons.