Susana Pinto

Towards a diagnostic test for sporadic ALS utilising deep learning and SNP microarrays

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 oligogenic structure of amyotrophic lateral sclerosis has genetic testing, counselling, and therapeutic implications

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.

Computing Linkage Disequilibrium Aware Genome Embeddings using Autoencoders

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

Genetic and phenotype analyses of primary lateral sclerosis datasets from international cohorts

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

Large-scale Analyses of CAV1 and CAV2 Suggest Their Expression is Higher in Post-mortem ALS Brain Tissue and Affects Survival

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.

Mutations in the tail domain of the neurofilament heavy chain gene increase the risk of amyotrophic lateral sclerosis

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.