New insights into translational research in Alzheimer's disease guided by artificial intelligence, computational and systems biology.
10.1016/j.apsb.2025.08.015
- Author:
Shulan JIANG
1
;
Zixi TIAN
1
;
Yuchen YANG
1
;
Xiang LI
1
;
Feiyan ZHOU
1
;
Jianhua CHENG
2
;
Jihui LYU
1
;
Tingting GAO
1
;
Ping ZHANG
1
;
Hongbin HAN
3
;
Zhiqian TONG
1
Author Information
1. Beijing Geriatric Hospital, Beijing 100095, China.
2. Department of Neurology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325035, China.
3. Department of Radiology, Peking University Third Hospital, Key Laboratory of Magnetic Resonance Imaging Equipment and Technique, NMPA Key Laboratory for Evaluation of Medical Imaging Equipment and Technique, Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China.
- Publication Type:Review
- Keywords:
Alzheimer's disease;
Artificial intelligence;
Big biomedical data mining;
Computational biology;
Deep learning;
Drug development;
Machine learning;
Systems biology
- From:
Acta Pharmaceutica Sinica B
2025;15(10):5099-5126
- CountryChina
- Language:English
-
Abstract:
Alzheimer's disease (AD) is characterized by cognitive and functional deterioration, with pathological features such as amyloid-beta (Aβ) aggregates in the extracellular spaces of parenchymal neurons and intracellular neurofibrillary tangles formed by the hyperphosphorylation of tau protein. Despite a thorough investigation, current treatments targeting the reduction of Aβ production, promotion of its clearance, and inhibition of tau protein phosphorylation and aggregation have not met clinical expectations, posing a substantial obstacle in the development of drugs for AD. Recently, artificial intelligence (AI), computational biology (CB), and systems biology (SB) have emerged as promising methodologies in AD research. Their capacity to analyze extensive and varied datasets facilitates the identification of intricate patterns, thereby enriching our comprehension of AD pathology. This paper provides a comprehensive examination of the utilization of AI, CB, and SB in the diagnosis of AD, including the use of imaging omics for early detection, drug discovery methods such as lecanemab, and complementary therapies like phototherapy. This review offers novel perspectives and potential avenues for further research in the realm of translational AD studies.
