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

Frontiers in Aging Neuroscience

Aging Neuroscience

Yeojin Kim¹  Hyunju Lee² 

[1] Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea;Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea;School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea;

关键词: Alzheimer's disease;    machine learning;    transcriptomics;    biomarkers;    bioinformatics;    protein-protein interaction network;    interpretable machine learning;   

DOI  :  10.3389/fnagi.2023.1126156

 received in 2022-12-17, accepted in 2023-06-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

IntroductionIdentification 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.MethodsTo 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.ResultsThe performance of PINNet was compared 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.DiscussionBy integrating prior knowledge about pathways, PINNet can reveal essential pathways related to AD. The source codes are available at https://github.com/DMCB-GIST/PINNet.

【 授权许可】

Unknown   
Copyright © 2023 Kim and Lee.