Quadri Adewale

This study identifies miRNA signatures associated with resilience to Alzheimer's disease, revealing potential therapeutic targets for neuroprotection.

The paper shows how the distribution of non-neuronal cell types in the brain predicts tissue damage patterns in thirteen neurodegenerative conditions.

The paper uses computational models to identify transcriptomic signatures linked to Aβ and tau-induced neuronal dysfunction in Alzheimer's disease, highlighting immune and inflammatory processes.

The paper uses RNA velocity to identify synaptic and cell-cycle dysregulations in Alzheimer's disease that are not detected by gene expression alone.

This study develops a framework for estimating mental workload using wireless EEG signals, achieving high accuracy across tasks, subjects, and sessions.

This paper introduces a novel GE contrastive trajectory inference method to analyze gene expression patterns in neurodegenerative diseases, showing strong predictive power for neuropathology and cognitive decline using blood and brain data.