Jingqiang Wang

Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China
Author    Correspondence author
Computational Molecular Biology, 2025, Vol. 15, No. 5   
Received: 18 Aug., 2025    Accepted: 29 Sep., 2025    Published: 18 Oct., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease characterized by progressive cognitive decline, and understanding its complex molecular mechanisms remains a significant challenge. This review summarizes the application of computational network reconstruction methods to elucidate disease-related molecular interactions in the pathogenesis of AD; it systematically examines genetic, transcriptomic, and proteomic alterations leading to network dysregulation and reviews state-of-the-art algorithms for reconstructing and analyzing biological networks. Disease modules were identified through clustering and functional annotation, prioritizing candidate genes and pathways associated with AD. A case study of late-onset AD demonstrates how integrative network analysis can reveal novel associations between molecular components and clinical phenotypes. Despite these advances, challenges such as data heterogeneity, limited interpretability, and methodological limitations remain significant. This study highlights the powerful role of computational network-based frameworks in revealing the systemic organizational structure of Alzheimer's disease and predicts that future integration of single-cell omics and spatial omics, combined with AI-driven analysis, will provide deeper insights and facilitate the application of precision medicine for Alzheimer's disease.

Alzheimer’s disease; Computational network reconstruction; Disease modules; Systems biology; Bioinformatics