Computational Molecular Biology 2025, Vol.15, No.5, 254-264 http://bioscipublisher.com/index.php/cmb 261 to intercellular communication. It also provides new ideas for discovering potential biomarkers or drug action sites at specific locations (Wang et al., 2024). 7.2 AI and deep learning approaches When dealing with multi-omics data, researchers are increasingly relying on artificial intelligence, especially deep learning. However, the reason is not that traditional methods are completely unusable, but that when high-dimensional, sparse, and disordered data are analyzed together, the old methods often seem inadequate. Deep learning architectures such as graph convolutional networks and generative models are actually more capable of integrating different types of data into the same framework, making the model's judgment on cell states or disease phenotypes more stable. These methods also have a feature: they can simulate responses under different disturbances, thus giving researchers the opportunity to speculate on deeper mechanisms, which is particularly helpful when dealing with the high heterogeneity of AD. Of course, the improvement of interpretability is also very important. After all, no matter how strong a model is, if it cannot clearly explain "why", its credibility in clinical or biological terms will be greatly reduced. The development of explainable artificial intelligence in recent years is gradually improving this, making the conclusions of network inference more acceptable (Ji et al., 2021; Ge et al., 2024). 7.3 Translational applications At the application level, web-based analysis is mainly used to identify biomarkers, distinguish patient subtypes, and locate potential therapeutic targets. By integrating multi-omics data, spatial information and clinical phenotypes through artificial intelligence models, there is a chance to make the early diagnosis of AD more accurate and it is also easier to formulate individualized treatment strategies. Meanwhile, the network model can point to those key nodes that truly drive pathological changes, providing direction for experimental verification and drug development. As these computing tools gradually mature, the possibility of their being incorporated into clinical processes is also increasing, which is expected to promote the further implementation of precision medicine in the field of AD (Kim et al., 2023; Ballard et al., 2024). 8 Concluding Remarks In recent years, if we look back at the research on Alzheimer's disease (AD) from the perspective of network reconstruction, we will find that many molecular clues that were previously difficult to detect have gradually been pieced together. The addition of multi-omics data continuously brings new candidate genes and pathways. Familiar pathways such as immune signals and neuroinflammation have once again been brought to the forefront, all seemingly promoting the disease process to varying degrees. However, the conclusions drawn by network models do not always revolve around those "most prominent" genes. Many peripheral genes play a key role at certain stages, which also makes the traditional assumptions about gene centrality seem less solid. These results to some extent highlight the variability and regional specificity of AD pathology, and also remind us that a static and coarse-grained perspective is difficult to truly understand the hierarchical structure of AD. Without higher-resolution data and computational methods that can flexibly handle complex relationships, these dynamic changes would have been very easy to be overlooked. Web-based analysis has also brought about several particularly notable results. For instance, regional functional disorders presented at different disease stages, the emergence of major regulatory factors such as JMJD6 and VGF, as well as the stable inflammation-related molecular network between microglia and astrocytes. These clues become clearer when combined with single-cell and batch transcriptome data, and the graph diffusion and causal inference methods further enhance the robustness of network reconstruction. It is precisely these advancements that have made it more realistic to search for biomarkers and potential therapeutic targets that can reflect the multi-factor characteristics of AD. To truly understand the complexity of AD, interdisciplinary cooperation is still indispensable. Computational biology can only provide one entry point. When combined with the results of neurogenetics, clinical research and experimental verification, the biological significance of the network model will be more reliable. Analyzing large-scale genetic data, imaging materials and clinical phenotypes together can help enhance the translational
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