Teresa Wu

Interpretable deep learning framework for understanding molecular changes in human brains with Alzheimer's disease: implication for microglia activation and sex differences

This paper uses an interpretable deep learning framework to analyze transcriptomic data from Alzheimer's disease brain tissues, identifying gene signatures and sex-specific modules associated with microglia activation and neuronal loss.

Predicting RNA Sequence-Structure Likelihood via Structure-Aware Deep Learning

The paper develops two deep learning models to evaluate RNA sequence-structure pairs, showing improved performance over existing methods.

FEDERATED MORPHOMETRY FEATURE SELECTION FOR HIPPOCAMPAL MORPHOMETRY ASSOCIATED BETA-AMYLOID AND TAU PATHOLOGY

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.