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Current Genomics


ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Review Article

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis

Author(s): Xiong Li*, Yangping Qiu, Juan Zhou and Ziruo Xie

Volume 22, Issue 8, 2021

Published on: 31 December, 2021

Page: [564 - 582] Pages: 19

DOI: 10.2174/1389202923666211216163049

Price: $65


Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data.

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.

Keywords: Alzheimer's disease, machine learning, association analysis, disease diagnosis, multi-modal data fusion, genomewide.

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