Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer’s disease

Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML...

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Autores principales:	Jack Cheng, Hsin-Ping Liu, Wei-Yong Lin, Fuu-Jen Tsai
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b614a110173d4f4ab37eb591658964f1
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spelling	oai:doaj.org-article:b614a110173d4f4ab37eb591658964f12021-12-02T16:10:36ZMachine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer’s disease10.1038/s41598-021-93085-z2045-2322https://doaj.org/article/b614a110173d4f4ab37eb591658964f12021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93085-zhttps://doaj.org/toc/2045-2322Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML) is so far unrealized, majorly due to the lack of needed data density. To harness the AD mechanism's knowledge from the expression profiles of postmortem prefrontal cortex samples of 310 AD and 157 controls, we used seven predictive operators or combinations of RapidMiner Studio operators to establish predictive models from the input matrix and to assign a weight to each attribute. Besides, conventional fold-change methods were also applied as controls. The identified genes were further submitted to enrichment analysis for KEGG pathways. The average accuracy of ML models ranges from 86.30% to 91.22%. The overlap ratio of the identified genes between ML and conventional methods ranges from 19.7% to 21.3%. ML exclusively identified oxidative phosphorylation genes in the AD pathway. Our results highlighted the deficiency of oxidative phosphorylation in AD and suggest that ML should be considered as complementary to the conventional fold-change methods in transcriptome studies.Jack ChengHsin-Ping LiuWei-Yong LinFuu-Jen TsaiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jack Cheng Hsin-Ping Liu Wei-Yong Lin Fuu-Jen Tsai Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer’s disease
description	Abstract Alzheimer’s disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML) is so far unrealized, majorly due to the lack of needed data density. To harness the AD mechanism's knowledge from the expression profiles of postmortem prefrontal cortex samples of 310 AD and 157 controls, we used seven predictive operators or combinations of RapidMiner Studio operators to establish predictive models from the input matrix and to assign a weight to each attribute. Besides, conventional fold-change methods were also applied as controls. The identified genes were further submitted to enrichment analysis for KEGG pathways. The average accuracy of ML models ranges from 86.30% to 91.22%. The overlap ratio of the identified genes between ML and conventional methods ranges from 19.7% to 21.3%. ML exclusively identified oxidative phosphorylation genes in the AD pathway. Our results highlighted the deficiency of oxidative phosphorylation in AD and suggest that ML should be considered as complementary to the conventional fold-change methods in transcriptome studies.
format	article
author	Jack Cheng Hsin-Ping Liu Wei-Yong Lin Fuu-Jen Tsai
author_facet	Jack Cheng Hsin-Ping Liu Wei-Yong Lin Fuu-Jen Tsai
author_sort	Jack Cheng
title	Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer’s disease
title_short	Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer’s disease
title_full	Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer’s disease
title_fullStr	Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer’s disease
title_full_unstemmed	Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer’s disease
title_sort	machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of alzheimer’s disease
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b614a110173d4f4ab37eb591658964f1
work_keys_str_mv	AT jackcheng machinelearningcompensatesfoldchangemethodandhighlightsoxidativephosphorylationinthebraintranscriptomeofalzheimersdisease AT hsinpingliu machinelearningcompensatesfoldchangemethodandhighlightsoxidativephosphorylationinthebraintranscriptomeofalzheimersdisease AT weiyonglin machinelearningcompensatesfoldchangemethodandhighlightsoxidativephosphorylationinthebraintranscriptomeofalzheimersdisease AT fuujentsai machinelearningcompensatesfoldchangemethodandhighlightsoxidativephosphorylationinthebraintranscriptomeofalzheimersdisease
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Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer’s disease

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