Nazem Atassi

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

This study uses 7T fMRI to identify disrupted functional connectivity in ALS patients, particularly in cerebellar lobule VI, suggesting its involvement in ALS pathology.

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

This study identifies genetic variants and networks associated with ALS using GWAS data, revealing novel loci and functional sub-networks linked to disease.

This study uses a crowdsourcing approach to stratify ALS patients, identifying four distinct clusters with unique predictors and improving outcome prediction.