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

INTRODUCTION: 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 inte...

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Detalles Bibliográficos
Autores principales: Kim, Yeojin, Lee, Hyunju
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10380929/
https://www.ncbi.nlm.nih.gov/pubmed/37520124
http://dx.doi.org/10.3389/fnagi.2023.1126156
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author Kim, Yeojin
Lee, Hyunju
author_facet Kim, Yeojin
Lee, Hyunju
author_sort Kim, Yeojin
collection PubMed
description INTRODUCTION: 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. METHODS: 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. RESULTS: The 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. DISCUSSION: By 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.
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spelling pubmed-103809292023-07-29 PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease Kim, Yeojin Lee, Hyunju Front Aging Neurosci Aging Neuroscience INTRODUCTION: 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. METHODS: 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. RESULTS: The 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. DISCUSSION: By 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. Frontiers Media S.A. 2023-07-14 /pmc/articles/PMC10380929/ /pubmed/37520124 http://dx.doi.org/10.3389/fnagi.2023.1126156 Text en Copyright © 2023 Kim and Lee. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Aging Neuroscience
Kim, Yeojin
Lee, Hyunju
PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease
title PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease
title_full PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease
title_fullStr PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease
title_full_unstemmed PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease
title_short PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease
title_sort pinnet: a deep neural network with pathway prior knowledge for alzheimer's disease
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10380929/
https://www.ncbi.nlm.nih.gov/pubmed/37520124
http://dx.doi.org/10.3389/fnagi.2023.1126156
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