Computational Molecular Biology 2025, Vol.15, No.5, 254-264 http://bioscipublisher.com/index.php/cmb 254 Case Study Open Access Computational Reconstruction of Disease-Associated Networks in Human Alzheimer's Pathogenesis Jingqiang Wang Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China Corresponding author: jingqiang.wang@jicat.org Computational Molecular Biology, 2025, Vol.15, No.5 doi: 10.5376/cmb.2025.15.0025 Received: 18 Aug., 2025 Accepted: 29 Sep., 2025 Published: 18 Oct., 2025 Copyright © 2025 Wang, 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.6 Preferred citation for this article: Wang J.Q., 2025, Computational reconstruction of disease-associated networks in human Alzheimer's pathogenesis, Computational Molecular Biology, 15(5): 254-264 (doi: 10.5376/cmb.2025.15.0025) Abstract 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. Keywords Alzheimer’s disease; Computational network reconstruction; Disease modules; Systems biology; Bioinformatics 1 Introduction Alzheimer's disease (AD) is often noticed by people, but it usually shows obvious memory decline or slow thinking. In fact, its asymptomatic stage can last for many years (Li and Zhang, 2024). By the time cognitive function begins to decline, pathological changes such as beta-amyloid (Aβ) plaques and tau protein tangles in the brain have quietly accumulated in regions such as the default mode network and the medial temporal lobe, and have affected neural connections and coordination between brain regions (Yu et al., 2021). These structural and functional changes are merely superficial. Behind them lies a series of disorders at the molecular, cellular and even genetic levels, interwoven into a disease picture that is difficult to explain from a single perspective. Although research on AD has been ongoing for many years, the molecular mechanism of sporadic AD has not been truly clarified, which still makes the development of treatment difficult to this day. The progression of diseases is often not as simple as a problem with a single gene or pathway, but rather the result of the superposition of multiple factors at different levels. Some patients show a rapid progression while others do so slowly. This individual difference also suggests that AD does not follow a fixed pathological "path". Against this backdrop, network-based computing methods have gradually been adopted by more researchers. They attempt to put together data from multiple omics, gene expression and brain connections, no longer focusing only on a single gene, but observing broader interactions. In this way, regulatory factors, molecular pathways or cell types that act as "key nodes" in AD can be identified, and signals that are easily overlooked by traditional analysis can be captured in the dynamic changes of the network (Beckmann et al., 2020; Xu et al., 2020; Merchant et al., 2023). Of course, such methods are not omnipotent, but at least they provide an entry point closer to real biological systems for understanding complex diseases. This study will focus on these network-based computing strategies, with a particular emphasis on the research progress made in network reconstruction of the molecular mechanisms of human AD in recent years. This study
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