Kewei Chen

SAKPE predicts enzyme kinetic parameters with improved accuracy by incorporating weighted protein representations, outperforming existing methods in kcat, Km, and Ki predictions.

This paper develops and validates an integrated model combining MRI and blood-based biomarkers to predict brain amyloid PET positivity with high accuracy.

The paper uses deep learning to analyze RNA-seq data from Alzheimer's disease brain donors, identifying transcriptomic signatures linked to neuropathology and clinical severity.

This paper introduces FMFS, a framework for selecting hippocampal morphometry features linked to Aβ/tau pathology, showing it outperforms existing methods and identifies key subregions for biomarker development.

This paper develops a method to predict brain amyloid burden using MRI-based morphometry statistics and sparse coding, achieving high accuracy in ADNI and OASIS cohorts.

This study uses an epidemic spreading model to show that tau spreads through neuronal connections in Alzheimer's disease, with beta-amyloid accelerating this process in some brain regions.

This study compares whole hippocampus and subfield biomarkers for Alzheimer’s disease detection and prediction using multimodal MRI data, finding subiculum biomarkers improve prediction accuracy.

This study compares cognitive decline rates in Alzheimer's disease with and without clinically undiagnosed Lewy body disease, finding faster decline in those with undiagnosed Lewy body disease.

The paper examines the heritability of regional brain volumes using genetic and neuroimaging data from multiple cohorts, finding high heritability and consistent patterns across populations.