Monika Ahirwar

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

This paper uses machine learning to identify cellular signatures in human motor neurons that distinguish TDP-43 ALS subtypes and enable patient stratification.

This paper develops a deep learning pipeline to automate amyloid pathology quantification in Alzheimer's disease, achieving human-level accuracy and enabling high-throughput analysis.