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Identification of Alzheimer's Disease (AD)-related transcriptomic signaturesfrom blood is important for early diagnosis of the disease. Deep learningtechniques are potent classifiers for AD diagnosis, but most have been unableto identify biomarkers because of their lack of interpretability. To addressthese challenges, we propose a pathway information-based neural network(PINNet) to predict AD patients and analyze blood and brain transcriptomicsignatures using an interpretable deep learning model. PINNet is a deep neuralnetwork (DNN) model with pathway prior knowledge from either the Gene Ontologyor Kyoto Encyclopedia of Genes and Genomes databases. Then, abackpropagation-based model interpretation method was applied to revealessential pathways and genes for predicting AD. We compared the performance ofPINNet with a DNN model without a pathway. Performances of PINNet outperformedor were similar to those of DNN without a pathway using blood and brain geneexpressions, respectively. Moreover, PINNet considers more AD-related genes asessential features than DNN without a pathway in the learning process. Pathwayanalysis of protein-protein interaction modules of highly contributed genesshowed that AD-related genes in blood were enriched with cell migration,PI3K-Akt, MAPK signaling, and apoptosis in blood. The pathways enriched in thebrain module included cell migration, PI3K-Akt, MAPK signaling, apoptosis,protein ubiquitination, and t-cell activation. Collectively, with priorknowledge about pathways, PINNet reveals essential pathways related to AD.

PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease