Computational Molecular Biology 2025, Vol.15, No.5, 254-264 http://bioscipublisher.com/index.php/cmb 262 value of the model. In addition, introducing analyses of different populations, including rare variations, can also help make up for the current deficiencies in understanding the genetic structure of AD. For network prediction to truly play a role in the future, the closed loop between computational research and experimental verification is of vital importance. Looking ahead, researchers have set their sights on spatial omics and explainable artificial intelligence, and these two types of technologies seem likely to open up new breakthroughs. Their value does not lie in "replacing" existing methods, but in being able to fill in many details that were previously unclear from the spatiotemporal dimension, making the trajectory of AD pathological changes more definite. Of course, the integration framework is still being improved, but as the coordination among data types increases, the factors that truly drive diseases may be identified more accurately, and the direction of drug reuse will also become clearer accordingly. However, no matter how powerful the computational model is, it cannot do without the coordination of experimental results and clinical observations. If these types of information can eventually be linked together, the diagnosis, prognosis and even treatment strategies of AD will mostly develop towards individualized routes that better reflect the heterogeneity of the disease. Acknowledgments I am very grateful to Ms. Guo for critically reading the manuscript and her meticulous proofreading work improved the clarity of the text. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Bai B., Wang X., Li Y., Chen P., Yu K., Dey K., Yarbro J., Han X., Lutz B., Rao S., Jiao Y., Sifford J., Han J., Wang M., Tan H., Shaw T., Cho J., Zhou S., Wang H., Niu M., Mancieri A., Messler K., Sun X., Wu Z., Pagala V., High A., Bi W., Zhang H., Chi H., Haroutunian V., Zhang B., Beach T., Yu G., and Peng J., 2020, Deep multilayer brain proteomics identifies molecular networks in Alzheimer’s disease progression, Neuron, 105: 975-991.e7. https://doi.org/10.1016/j.neuron.2019.12.015 Ballard J., Wang Z., Li W., Shen L., and Long Q., 2024, Deep learning-based approaches for multi-omics data integration and analysis, BioData Mining, 17(1): 38. https://doi.org/10.1186/s13040-024-00391-z Beckmann N., Lin W., Wang M., Cohain A., Charney A., Wang P., Wang W., Wang Y., Jiang C., Audrain M., Comella P., Fakira A., Hariharan S., Belbin G., Girdhar K., Levey A., Seyfried N., Dammer E., Duong D., Lah J., Haure-Mirande J., Shackleton B., Fanutza T., Blitzer R., Kenny E., Zhu J., Haroutunian V., Katsel P., Gandy S., Tu Z., Ehrlich M., Zhang B., Salton S., and Schadt E., 2020, Multiscale causal networks identify VGF as a key regulator of Alzheimer’s disease, Nature Communications, 11(1): 3942. https://doi.org/10.1038/s41467-020-17405-z Bi X., Yang Z., Chen D., Wang J., Xing Z., and Xu L., 2025, FHG-GAN: fuzzy hypergraph generative adversarial network with large foundation models for Alzheimer’s disease risk prediction, IEEE Transactions on Fuzzy Systems, 33(8): 2599-2613. https://doi.org/10.1109/TFUZZ.2025.3572479 Cruz A., De Anda-Jáuregui G., and Hernandez-Lemus E., 2025, Gene co-expression analysis reveals functional differences between early- and late-onset Alzheimer’s disease, Current Issues in Molecular Biology, 47(3): 200. https://doi.org/10.3390/cimb47030200 Cui W., Long Q., Xiao M., Wang X., Feng G., Li X., Wang P., and Zhou Y., 2024, Refining computational inference of gene regulatory networks: integrating knockout data within a multi-task framework, Briefings in Bioinformatics, 25(5): bbae361. https://doi.org/10.1093/bib/bbae361 Delgado-Chaves F., and Gómez-Vela F., 2019, Computational methods for gene regulatory networks reconstruction and analysis: a review, Artificial Intelligence in Medicine, 95: 133-145. https://doi.org/10.1016/j.artmed.2018.10.006 Efremova M., and Teichmann S., 2020, Computational methods for single-cell omics across modalities, Nature Methods, 17: 14-17. https://doi.org/10.1038/s41592-019-0692-4 Gao J., Bi X., Jiang W., and Wang Y., 2025, Integration of multi-omics quantitative trait loci evidence reveals novel susceptibility genes for Alzheimer’s disease, Scientific Reports, 15(1): 30158. https://doi.org/10.1038/s41598-025-12290-2 Ge S., Sun S., Xu H., Cheng Q., and Ren Z., 2024, Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective, Briefings in Bioinformatics, 26(2): bbaf136. https://doi.org/10.1093/bib/bbaf136
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