Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future...

Area of Science:

• Neuroscience
• Bioinformatics
• Computational Biology

Background:

• Alzheimer's disease (AD) is a progressive neurodegenerative disorder with limited effective treatments.
• Its multifactorial nature (genetic, environmental, biological) complicates research and clinical management.
• AI and multi-omics offer novel avenues for understanding AD's molecular underpinnings.

Purpose of the Study:

• To systematically review recent advances in AI-driven multi-omics research for Alzheimer's disease.
• To highlight achievements in early diagnosis and biomarker discovery.
• To discuss limitations and future directions for clinical translation.

Main Methods:

• Integration of large-scale multi-omics datasets (genomic, transcriptomic, proteomic, metabolomic, microbiomic) using AI.
• Application of machine learning, deep learning, and network-based models.
• Utilizing methods like deep belief networks and joint deep semi-non-negative matrix factorization for classification and stratification.

Main Results:

• AI-driven multi-omics approaches have facilitated the discovery of novel molecular signatures and potential therapeutic targets in AD.
• Improvements in disease classification and patient stratification have been observed.
• Significant progress has been made in identifying early diagnostic and prognostic biomarkers.

Conclusions:

• AI-powered multi-omics integration holds significant promise for advancing Alzheimer's disease research and clinical practice.
• Overcoming challenges like data heterogeneity, model interpretability, and data standardization is crucial for future progress.
• Further clinical validation is essential to translate these research findings into effective patient care.