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

Area of Science:

• Computational neuroscience
• Bioinformatics
• Medical imaging analysis

Background:

• Neuroimaging and genetic technologies enable in vivo measurement of Alzheimer's disease (AD) pathological features.
• High-dimensional, multi-modal neuroimaging and omics data analysis is crucial for identifying AD biomarkers.
• Machine learning (ML) methods are increasingly applied to large-scale AD biomedical data for discovering pathogenic mutations and mechanisms.

Purpose of the Study:

• To review and summarize the applications of ML in analyzing multi-source data for Alzheimer's disease.
• To identify current challenges and future research directions in ML-driven AD data analysis.

Main Methods:

• Literature search conducted on Google Scholar, PubMed, and Web of Science.
• Keywords included Alzheimer's disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, and multi-omics data integration.
• Comprehensive review of ML techniques applied to AD neuroimaging and omics data.

Main Results:

• ML techniques are successfully applied to AD neuroimaging data processing.
• Progress in computational analysis methods for omics data (genome, proteome) in AD is demonstrated.
• ML methods for AD imaging analysis are summarized.

Conclusions:

• Emerging technologies focus on joint analysis of multi-modal neuroimaging and multi-omics data for AD.
• Outstanding issues and future research directions in ML for AD are highlighted.
• ML offers a powerful approach for advancing early diagnosis and understanding of AD pathogenesis.