Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate...

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Autores principales:	Nivedhitha Mahendran, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Chuan-Yu Chang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	deep learning Alzheimer’s disease gene selection gene expression molecular bio-markers Genetics QH426-470
Acceso en línea:	https://doaj.org/article/dc704992286b42d89f8755b3ef2b363e
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spelling	oai:doaj.org-article:dc704992286b42d89f8755b3ef2b363e2021-11-12T05:29:47ZImproving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning1664-802110.3389/fgene.2021.784814https://doaj.org/article/dc704992286b42d89f8755b3ef2b363e2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.784814/fullhttps://doaj.org/toc/1664-8021Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer’s dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.Nivedhitha MahendranP. M. Durai Raj VincentKathiravan SrinivasanChuan-Yu ChangFrontiers Media S.A.articledeep learningAlzheimer’s diseasegene selectiongene expressionmolecular bio-markersGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning Alzheimer’s disease gene selection gene expression molecular bio-markers Genetics QH426-470
spellingShingle	deep learning Alzheimer’s disease gene selection gene expression molecular bio-markers Genetics QH426-470 Nivedhitha Mahendran P. M. Durai Raj Vincent Kathiravan Srinivasan Chuan-Yu Chang Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning
description	Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer’s dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.
format	article
author	Nivedhitha Mahendran P. M. Durai Raj Vincent Kathiravan Srinivasan Chuan-Yu Chang
author_facet	Nivedhitha Mahendran P. M. Durai Raj Vincent Kathiravan Srinivasan Chuan-Yu Chang
author_sort	Nivedhitha Mahendran
title	Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning
title_short	Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning
title_full	Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning
title_fullStr	Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning
title_full_unstemmed	Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning
title_sort	improving the classification of alzheimer’s disease using hybrid gene selection pipeline and deep learning
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/dc704992286b42d89f8755b3ef2b363e
work_keys_str_mv	AT nivedhithamahendran improvingtheclassificationofalzheimersdiseaseusinghybridgeneselectionpipelineanddeeplearning AT pmdurairajvincent improvingtheclassificationofalzheimersdiseaseusinghybridgeneselectionpipelineanddeeplearning AT kathiravansrinivasan improvingtheclassificationofalzheimersdiseaseusinghybridgeneselectionpipelineanddeeplearning AT chuanyuchang improvingtheclassificationofalzheimersdiseaseusinghybridgeneselectionpipelineanddeeplearning
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Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

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