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Open Access

Peer-reviewed

Research Article

Wide and deep learning based approaches for classification of Alzheimer’s disease using genome-wide association studies

Abbas Saad Alatrany ,

Roles Conceptualization, Methodology, Software, Validation, Writing – original draft

* E-mail: a.s.alatrany@2020.ljmu.ac.uk

Affiliations School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom, University of Information Technology and Communications, Baghdad, Iraq, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq

https://orcid.org/0000-0002-4504-1506

⨯
Wasiq Khan,

Roles Investigation, Methodology, Validation, Writing – review & editing

Affiliation School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom

https://orcid.org/0000-0002-7511-3873

⨯
Abir Hussain,

Roles Investigation, Writing – review & editing

Affiliations School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom, Department of Electrical Engineering, University of Sharjah, Sharjah, UAE
⨯
Dhiya Al-Jumeily,

Roles Project administration, Supervision, Writing – review & editing

Affiliation School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, United Kingdom

https://orcid.org/0000-0002-9170-0568

⨯
for the Alzheimer’s Disease Neuroimaging Initiative

Wide and deep learning based approaches for classification of Alzheimer’s disease using genome-wide association studies

Abbas Saad Alatrany,
Wasiq Khan,
Abir Hussain,
Dhiya Al-Jumeily,
for the Alzheimer’s Disease Neuroimaging Initiative

x

Published: May 1, 2023
https://doi.org/10.1371/journal.pone.0283712

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Genome-wide association studies
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Alzheimer's disease
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Single nucleotide polymorphisms
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Algorithms
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Machine learning
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Artificial neural networks
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Principal component analysis
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Neurons
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