Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease

Background: There are no obvious clinical signs and symptoms in the early stages of Alzheimer’s disease (AD), and most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, early diagnosis of AD is very critical. This paper mainly discusses the blood biomarkers of AD pat...

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Autores principales:	Yuan Sh, Benliang Liu, Jianhu Zhang, Ying Zhou, Zhiyuan Hu, Xiuli Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	Alzheimer’s disease mild cognitive impairment artificial intelligence predictive diagnostics blood biomarkers Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/39de30a2fedb48b8bac7173c6e7dc2a8
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spelling	oai:doaj.org-article:39de30a2fedb48b8bac7173c6e7dc2a82021-12-03T14:22:43ZApplication of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease1663-436510.3389/fnagi.2021.768229https://doaj.org/article/39de30a2fedb48b8bac7173c6e7dc2a82021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnagi.2021.768229/fullhttps://doaj.org/toc/1663-4365Background: There are no obvious clinical signs and symptoms in the early stages of Alzheimer’s disease (AD), and most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, early diagnosis of AD is very critical. This paper mainly discusses the blood biomarkers of AD patients and uses machine learning methods to study the changes of blood transcriptome during the development of AD and to search for potential blood biomarkers for AD.Methods: Individualized blood mRNA expression data of 711 patients were downloaded from the GEO database, including the control group (CON) (238 patients), MCI (189 patients), and AD (284 patients). Firstly, we analyzed the subcellular localization, protein types and enrichment pathways of the differentially expressed mRNAs in each group, and established an artificial intelligence individualized diagnostic model. Furthermore, the XCell tool was used to analyze the blood mRNA expression data and obtain blood cell composition and quantitative data. Ratio characteristics were established for mRNA and XCell data. Feature engineering operations such as collinearity and importance analysis were performed on all features to obtain the best feature solicitation. Finally, four machine learning algorithms, including linear support vector machine (SVM), Adaboost, random forest and artificial neural network, were used to model the optimal feature combinations and evaluate their classification performance in the test set.Results: Through feature engineering screening, the best feature collection was obtained. Moreover, the artificial intelligence individualized diagnosis model established based on this method achieved a classification accuracy of 91.59% in the test set. The area under curve (AUC) of CON, MCI, and AD were 0.9746, 0.9536, and 0.9807, respectively.Conclusion: The results of cell homeostasis analysis suggested that the homeostasis of Natural killer T cell (NKT) might be related to AD, and the homeostasis of Granulocyte macrophage progenitor (GMP) might be one of the reasons for AD.Yuan ShBenliang LiuBenliang LiuJianhu ZhangYing ZhouZhiyuan HuZhiyuan HuZhiyuan HuZhiyuan HuXiuli ZhangFrontiers Media S.A.articleAlzheimer’s diseasemild cognitive impairmentartificial intelligencepredictive diagnosticsblood biomarkersNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Aging Neuroscience, Vol 13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Alzheimer’s disease mild cognitive impairment artificial intelligence predictive diagnostics blood biomarkers Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Alzheimer’s disease mild cognitive impairment artificial intelligence predictive diagnostics blood biomarkers Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Yuan Sh Benliang Liu Benliang Liu Jianhu Zhang Ying Zhou Zhiyuan Hu Zhiyuan Hu Zhiyuan Hu Zhiyuan Hu Xiuli Zhang Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease
description	Background: There are no obvious clinical signs and symptoms in the early stages of Alzheimer’s disease (AD), and most patients usually have mild cognitive impairment (MCI) before diagnosis. Therefore, early diagnosis of AD is very critical. This paper mainly discusses the blood biomarkers of AD patients and uses machine learning methods to study the changes of blood transcriptome during the development of AD and to search for potential blood biomarkers for AD.Methods: Individualized blood mRNA expression data of 711 patients were downloaded from the GEO database, including the control group (CON) (238 patients), MCI (189 patients), and AD (284 patients). Firstly, we analyzed the subcellular localization, protein types and enrichment pathways of the differentially expressed mRNAs in each group, and established an artificial intelligence individualized diagnostic model. Furthermore, the XCell tool was used to analyze the blood mRNA expression data and obtain blood cell composition and quantitative data. Ratio characteristics were established for mRNA and XCell data. Feature engineering operations such as collinearity and importance analysis were performed on all features to obtain the best feature solicitation. Finally, four machine learning algorithms, including linear support vector machine (SVM), Adaboost, random forest and artificial neural network, were used to model the optimal feature combinations and evaluate their classification performance in the test set.Results: Through feature engineering screening, the best feature collection was obtained. Moreover, the artificial intelligence individualized diagnosis model established based on this method achieved a classification accuracy of 91.59% in the test set. The area under curve (AUC) of CON, MCI, and AD were 0.9746, 0.9536, and 0.9807, respectively.Conclusion: The results of cell homeostasis analysis suggested that the homeostasis of Natural killer T cell (NKT) might be related to AD, and the homeostasis of Granulocyte macrophage progenitor (GMP) might be one of the reasons for AD.
format	article
author	Yuan Sh Benliang Liu Benliang Liu Jianhu Zhang Ying Zhou Zhiyuan Hu Zhiyuan Hu Zhiyuan Hu Zhiyuan Hu Xiuli Zhang
author_facet	Yuan Sh Benliang Liu Benliang Liu Jianhu Zhang Ying Zhou Zhiyuan Hu Zhiyuan Hu Zhiyuan Hu Zhiyuan Hu Xiuli Zhang
author_sort	Yuan Sh
title	Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease
title_short	Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease
title_full	Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease
title_fullStr	Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease
title_full_unstemmed	Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease
title_sort	application of artificial intelligence modeling technology based on fluid biopsy to diagnose alzheimer’s disease
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/39de30a2fedb48b8bac7173c6e7dc2a8
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Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer’s Disease

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