摘要
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-nonnegative 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.
基金
supported by the Natural Science Foundation of China(Nos.81903829,82304994)
the Open Project from the State Key Laboratory of Traditional Chinese Medicine Syndrome(No.SKLKY2024B0006,China)
the Sichuan Science and Technology Program(No.2024YFHZ0361,China)
the Science and Technology Strategic Cooperation Programs of Luzhou Municipal People’s Government and Southwest Medical University(No.2024LZXNYDJ026,China)
Chongqing Natural Science Foundation(CSTB2022NSGQ-MSX1560,China).
