Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.
10.1016/j.apsb.2025.07.030
- Author:
Fang REN
1
;
Jing WEI
2
;
Qingxin CHEN
2
;
Mengling HU
3
;
Lu YU
2
;
Jianing MI
4
;
Xiaogang ZHOU
2
;
Dalian QIN
2
;
Jianming WU
2
;
Anguo WU
2
Author Information
1. Chongqing Key Laboratory of Sichuan-Chongqing Co-construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Chongqing Traditional Chinese Medicine Hospital, Chongqing 400021, China.
2. Sichuan Key Medical Laboratory of New Drug Discovery and Drugability Evaluation, Key Laboratory of Medical Electrophysiology of Ministry of Education, School of Pharmacy, Department of Cardiology, Department of Ophthalmology, the Affiliated Hospital of Southwest Medical University, Southwest Medical University, Luzhou 646000, China.
3. Department of Pharmacy, Guang'an People's Hospital, Guangan 638550, China.
4. State Key Laboratory of Traditional Chinese Medicine Syndrome, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510120, China.
- Publication Type:Review
- Keywords:
Alzheimer's disease;
Artificial intelligence;
Biomarkers;
Deep learning;
Drug discovery;
Early detection;
Machine learning;
Multi-omics;
Pathological mechanisms;
Personalized treatment
- From:
Acta Pharmaceutica Sinica B
2025;15(9):4327-4385
- CountryChina
- Language:English
-
Abstract:
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with few effective treatments currently available. The multifactorial nature of AD, shaped by genetic, environmental, and biological factors, complicates both research and clinical management. Recent advances in artificial intelligence (AI) and multi-omics technologies provide new opportunities to elucidate the molecular mechanisms of AD and identify early biomarkers for diagnosis and prognosis. AI-driven approaches such as machine learning, deep learning, and network-based models have enabled the integration of large-scale genomic, transcriptomic, proteomic, metabolomic, and microbiomic datasets. These efforts have facilitated the discovery of novel molecular signatures and therapeutic targets. Methods including deep belief networks and joint deep semi-non-negative matrix factorization have contributed to improvements in disease classification and patient stratification. However, ongoing challenges remain. These include data heterogeneity, limited interpretability of complex models, a lack of large and diverse datasets, and insufficient clinical validation. The absence of standardized multi-omics data processing methods further restricts progress. This review systematically summarizes recent advances in AI-driven multi-omics research in AD, highlighting achievements in early diagnosis and biomarker discovery while discussing limitations and future directions needed to advance these approaches toward clinical application.
