Computational Molecular Biology 2025, Vol.15, No.5, 254-264 http://bioscipublisher.com/index.php/cmb 258 modules more relevant. However, as many genes are multi-functional and often participate in multiple processes simultaneously, overlapping community detection is particularly important as it can more truly reflect the multi-layered structure of the AD network itself. The evaluation of GWAS data also shows that these improved methods can indeed identify the modules highly enriched with AD genetic signals (Zhou et al., 2021). 4.2 Prioritizing candidate genes After the module is identified, the next common question is: Which genes are more worthy of attention? Priority ranking usually combines information on network structure, multi-omics evidence and gene function to determine its potential role in AD. For instance, machine learning methods such as semi-supervised non-negative matrix factorization take into account factors like the relationships and proximity within modules. Even if the network data is incomplete, they can still enhance the accuracy of candidate gene screening. Online scoring does not merely look at the evidence of a single gene, but simultaneously assesses its relationship with functionally similar genes. This approach makes it easier to identify the molecules that may truly influence disease progression. After combining the information of genetic association, expression changes and protein interactions, researchers successfully screened out a variety of genes closely related to AD, such as PSEN1, APP, ABCA7, etc. (Yang et al., 2021). 4.3 Functional annotation of modules After identifying the modules, the next step is usually to figure out exactly what these modules are "doing". Functional annotations mainly determine the biological processes or pathways involved in the module through enrichment analysis. Modules related to AD often focus on neurogenesis, synaptic signaling, immune responses or metabolism, all of which are consistent with the known characteristics of the disease. Functional annotations can not only help researchers associate modules with specific AD phenotypes, but also provide new hypotheses for subsequent experimental verification. In recent years, some multilayer methods that combine ontological terms with network inference results have been proven to improve the consistency of annotations and be more helpful in identifying hub genes that may become key regulatory points or drug targets of AD (Jha et al., 2020). 5 Case Study: Network-Based Insights into Late-Onset AD Research on late-onset Alzheimer's disease (LOAD) is often difficult to understand from a single perspective, as the influences of genetics, epigenetics, metabolism and living environment are often mixed together. To gain a more comprehensive understanding of how these factors interact, researchers usually incorporate multi-level data into network models rather than merely focusing on a single gene or image. Network analysis can categorize disease-related functional modules at the system level, making it easier to detect some hidden pathways or potential targets (Figure 2) (Sarma and Chatterjee, 2024). This type of method may not solve all problems, but it does offer a different perspective on observing the molecular basis of LOAD. 5.1 Dataset selection and preprocessing When conducting LOAD network analysis, the commonly used datasets mostly come from queues that have been tracked for a long time, such as ROSMAP or ADNI. These data sets integrate genomic, epigenomic, proteomic, imaging and clinical manifestation information, but often require a lot of processing before use. Normalization, noise filtering and feature selection are the basic steps, mainly to enable data from different sources to run within the same analytical framework. Recent studies have also particularly reminded that choosing samples with sufficient information and reducing technical differences through batch correction can make the final constructed network more stable and better interpretable (Cruz et al., 2025; Zhou et al., 2025). 5.2 Construction and analysis of AD networks When studying late-onset AD (LOAD), the construction of the network usually does not start from a single type of data. Researchers often need to incorporate gene co-expression, protein interaction, and the connection conditions between different brain regions into the analytical framework; otherwise, it is very easy to overlook key details. As methods such as single-sample network inference and dynamic collaborative indicators gradually mature, the changes that occur at different stages of LOAD have become easier to capture, such as functional disorders in certain brain regions at an early stage, or the transmission paths of tau protein in different regions are not the same.
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