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

TL;DR

PINNet is an interpretable deep neural network that integrates pathway prior knowledge to improve Alzheimer's disease prediction from blood and brain transcriptomic data, revealing key biological pathways involved in AD.

Contribution

This paper introduces PINNet, a novel pathway-informed neural network that enhances AD prediction accuracy and interpretability by incorporating pathway prior knowledge from biological databases.

Findings

PINNet outperforms or matches traditional DNN in AD prediction.

PINNet identifies more AD-related genes as essential features.

Pathway analysis reveals key biological processes involved in AD.

Abstract

Identification of Alzheimer's Disease (AD)-related transcriptomic signatures from blood is important for early diagnosis of the disease. Deep learning techniques are potent classifiers for AD diagnosis, but most have been unable to identify biomarkers because of their lack of interpretability. To address these challenges, we propose a pathway information-based neural network (PINNet) to predict AD patients and analyze blood and brain transcriptomic signatures using an interpretable deep learning model. PINNet is a deep neural network (DNN) model with pathway prior knowledge from either the Gene Ontology or Kyoto Encyclopedia of Genes and Genomes databases. Then, a backpropagation-based model interpretation method was applied to reveal essential pathways and genes for predicting AD. We compared the performance of PINNet with a DNN model without a pathway. Performances of PINNet outperformed or were similar to those of DNN without a pathway using blood and brain gene expressions, respectively. Moreover, PINNet considers more AD-related genes as essential features than DNN without a pathway in the learning process. Pathway analysis of protein-protein interaction modules of highly contributed genes showed that AD-related genes in blood were enriched with cell migration, PI3K-Akt, MAPK signaling, and apoptosis in blood. The pathways enriched in the brain module included cell migration, PI3K-Akt, MAPK signaling, apoptosis, protein ubiquitination, and t-cell activation. Collectively, with prior knowledge about pathways, PINNet reveals essential pathways related to AD.