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- W4200330357 abstract "Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research." @default.
- W4200330357 created "2021-12-31" @default.
- W4200330357 creator A5025050406 @default.
- W4200330357 creator A5035545488 @default.
- W4200330357 creator A5044987134 @default.
- W4200330357 creator A5055458864 @default.
- W4200330357 date "2021-12-09" @default.
- W4200330357 modified "2023-09-30" @default.
- W4200330357 title "Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data" @default.
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