Mónica Povedano

The paper investigates WAT dysfunction in TDP-43A315T ALS mice and finds early histological changes and altered leptin levels in human ALS patients.

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.

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.

This study identifies a differential lipidomic signature in nuclei of ALS patients, linking lipid changes to disease mechanisms.