Can I read Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data on EtoBox?

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data by Ziyi Li; Xiaoqian Jiang; Yizhuo Wang; Yejin Kim is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data about?

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 abou

Who reads Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data?

Author

Ziyi Li; Xiaoqian Jiang; Yizhuo Wang; Yejin Kim

Publisher

Portland Press Ltd.

Published

2021

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)