Article: Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis.
2022 Volume 22, Issue 8, Page(s) 564–582
Abstract: Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) : Objective: To introduce and summarize the applications and challenges of ...
Abstract | Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) Objective: To introduce and summarize the applications and challenges of machine learning methods in Alzheimer's disease multi-source data analysis. Methods: The literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer's disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on. Conclusion: This study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions. |
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Language | English |
Publishing date | 2022-04-07 |
Publishing country | United Arab Emirates |
Document type | Journal Article ; Review |
ZDB-ID | 2033677-9 |
ISSN | 1875-5488 ; 1389-2029 |
ISSN (online) | 1875-5488 |
ISSN | 1389-2029 |
DOI | 10.2174/1389202923666211216163049 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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