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

ABSTRACT:

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 literatur

SUBMITTER: Li X 

PROVIDER: S-EPMC8922327 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature