Computational Molecular Biology 2025, Vol.15, No.5, 254-264 http://bioscipublisher.com/index.php/cmb 259 These analyses often draw attention to several fixed brain regions, such as the entorhinal cortex and the parahippocampal gyrus, which are more like hubs in the network, and any slight fluctuation will affect the surrounding structures. Looking further down at the results at the module level, many familiar pathways can be seen: neurodegeneration, synaptic related signals, immune responses... These are all core processes that repeatedly occur in LOAD (Lee et al., 2022; Zhang et al., 2025). Figure 2 AD biomarkers (Adopted from Sarma and Chatterjee, 2024) 5.3 Biological and clinical interpretation After the network construction is completed, a common problem is how to map these molecular-level clues to clinical manifestations. The network of LOAD often reflects the combined influence of multiple risk factors, such as APOE genotype, age or lifestyle, which can make the brain regions responsible for memory, sensory integration or emotion regulation more vulnerable to damage. Machine learning models trained on these networks can usually make more accurate diagnoses or disease course staging than single indicators, and are also more suitable for individualized prediction. In more cases, this online information can also help explain why LOAD behaves divergenously and provide references for formulating more precise intervention strategies (Venugopalan et al., 2021; Winter et al., 2024). 6 Challenges and Limitations When attempting to reconstruct the network related to Alzheimer's disease (AD) using computational methods, researchers often encounter problems with the data itself first. Although various omics, imaging and clinical information are all important, the noise, missing values and batch differences among them are often inconsistent, which makes network inference particularly tricky. Due to different sample sources, processing procedures, and the inherent diversity of the population, these factors can easily "drown out" the true disease signals, resulting in low repeatability. Even if the situation is improved through standardization, batch correction or more advanced methods, the residual heterogeneity is still difficult to be completely eliminated, which also makes the construction of stable and reliable network models more challenging (Wang et al., 2022).
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