Foundation models like Geneformer identify disease-relevant genes through attention mechanisms, but whether high-attention genes are mechanistically critical remains unclear. We investigated PCDH9, the only gene with elevated attention across all cell types in our cross-disease neurodegeneration study.
Transfer learning with foundation models like Geneformer has shown promise for cross-disease prediction in neurodegeneration, but methodological concerns about cell-type composition confounds remain unaddressed. We conducted cell-type stratified experiments across Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), fine-tuning Geneformer within four homogeneous cell populations.
Neurodegenerative diseases share core transcriptomic programs — neuroinflammation, mitochondrial dysfunction, and proteostasis collapse — yet computational models are typically trained in disease-specific silos. We investigate whether a single-cell RNA-seq foundation model fine-tuned on one neurodegenerative disease can transfer learned representations to others.
Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disorder characterized by progressive loss of motor neurons, leading to muscle weakness, paralysis, and ultimately death within 2-5 years of diagnosis. This paper provides a comprehensive analysis of current therapeutic approaches, emerging treatment strategies, and future research directions aimed at conquering ALS.
Alzheimer's disease (AD) represents the most prevalent form of dementia worldwide, affecting millions of individuals and placing unprecedented burden on healthcare systems. Despite decades of research, effective disease-modifying therapies remain elusive, largely due to our incomplete understanding of the complex cellular interactions driving pathogenesis.