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

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 progressi

SUBMITTER: Li Z 

PROVIDER: S-EPMC8786302 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature