Marc Gotkine

This paper evaluates combining expression-based surrogate variables and genotype-based principal components in differential expression analysis, finding the combined approach improves replicability and biological recall in ALS datasets.

The paper identifies seven sex-dependent differentially methylated positions in ALS blood samples, suggesting sex-specific biological mechanisms in the disease.

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 identifies sex-associated DNA methylation differences in ALS using a large-scale epigenome-wide association study and links them to survival outcomes.

This study shows SMCHD1 loss causes DUX4 expression in FSHD2 by disrupting splicing of DNMT3B, leading to hypomethylation and muscle abnormalities.

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.

The paper compares TDP-43's transcriptional and splicing effects in muscle and neuronal cells, showing tissue-specific roles in disease.

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.

The paper identifies de-novo SPTLC1 mutations in juvenile ALS patients and suggests serine supplementation may alleviate symptoms.

The paper identifies an enrichment of rare protein-truncating variants in ALS patients, highlighting novel genes like DNAJC7 and known ALS genes such as SOD1, NEK1, and FUS.

The paper uses unsupervised machine learning to identify three molecular subtypes in ALS cortex samples, associated with oxidative stress, retrotransposon activation, and glial activation.