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

Jay Shah

Abstract:

The objective of this study is to characterize the molecular changes associated with AD from gene expression data of brain tissues taking an interpretable deep learning approach that has not been fully exploited. We trained multi-layer perceptron (MLP) models for the classification of neuropathologically confirmed AD vs. controls using the transcriptomic data of three brain regions from the
ROSMAP study. The whole disease spectrum was then modeled as a progressive trajectory. SHAP (SHapley Additive exPlanations) value was derived to explain model predictions and identify significant implicated genes for subsequent network analysis of key gene modules underlying AD progression. The framework was validated using two external datasets: the Mayo RNA-seq study cohort and the Mount Sinai Brain Bank study cohort. The MLP models achieved superior performance in classification and prediction in external datasets. SHAP explainer revealed common and specific transcriptomic signatures from different brain regions. We identified common gene signatures in microglia and sex-specific modules in neurons that are implicated in AD. This work paves the way for utilizing artificial intelligence approaches in studying AD at the molecular level.