Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease
A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to disti...
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doaj-d166459c734e4a91855e5533777415912021-08-06T15:24:43ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672021-07-01227911791110.3390/ijms22157911Deep Learning with Neuroimaging and Genomics in Alzheimer’s DiseaseEugene Lin0Chieh-Hsin Lin1Hsien-Yuan Lane2Department of Biostatistics, University of Washington, Seattle, WA 98195, USAGraduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, TaiwanGraduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, TaiwanA growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.https://www.mdpi.com/1422-0067/22/15/7911Alzheimer’s diseaseartificial intelligencedeep learninggenomicsmachine learningmulti-omics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Eugene Lin Chieh-Hsin Lin Hsien-Yuan Lane |
spellingShingle |
Eugene Lin Chieh-Hsin Lin Hsien-Yuan Lane Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease International Journal of Molecular Sciences Alzheimer’s disease artificial intelligence deep learning genomics machine learning multi-omics |
author_facet |
Eugene Lin Chieh-Hsin Lin Hsien-Yuan Lane |
author_sort |
Eugene Lin |
title |
Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease |
title_short |
Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease |
title_full |
Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease |
title_fullStr |
Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease |
title_full_unstemmed |
Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease |
title_sort |
deep learning with neuroimaging and genomics in alzheimer’s disease |
publisher |
MDPI AG |
series |
International Journal of Molecular Sciences |
issn |
1661-6596 1422-0067 |
publishDate |
2021-07-01 |
description |
A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations. |
topic |
Alzheimer’s disease artificial intelligence deep learning genomics machine learning multi-omics |
url |
https://www.mdpi.com/1422-0067/22/15/7911 |
work_keys_str_mv |
AT eugenelin deeplearningwithneuroimagingandgenomicsinalzheimersdisease AT chiehhsinlin deeplearningwithneuroimagingandgenomicsinalzheimersdisease AT hsienyuanlane deeplearningwithneuroimagingandgenomicsinalzheimersdisease |
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1721218429886660608 |