Computational Molecular Biology 2024, Vol.14, No.1, 1-8 http://bioscipublisher.com/index.php/cmb 7 Additionally, with the widespread application of artificial intelligence in genomics, we are on the cusp of more intelligent genome prediction models. Through further optimization of machine learning algorithms, these models will be able to more accurately identify potential genetic associations and predict individual disease risks. This will provide clinicians with more reliable tools to better assist in decision-making and provide personalized medical advice. These findings will not only deepen our understanding of the mechanisms underlying Alzheimer's disease, but also hold promise for future treatment and prevention strategies. By combining the latest technologies and interdisciplinary research methods, significant achievements are expected in the near future. 4 Summary and Outlook Although genome prediction has made significant progress in Alzheimer's disease research, there are still a series of methodological challenges. The construction and optimization of models rely on large-scale genomic data. For complex diseases like Alzheimer's disease, larger and more diverse datasets are needed to improve the stability and generalization ability of models. Additionally, there are differences in methods and parameters used in different studies, which limits the consistency and comparability of results. Alzheimer's disease is a complex disease involving multiple factors and genetic markers, so the interpretability of genome prediction models becomes another challenge. Even if models perform well, we still have limited knowledge about the specific roles of specific genetic markers in the disease mechanism. This leads to difficulties in explaining prediction results, limiting the feasibility of genome prediction in clinical applications. Moreover, genome prediction models are typically based on population-level data, and there are significant individual differences. Personalized genome prediction models need to take into account individual lifestyles, environmental exposures, and other factors, which increases the complexity of data and interpretation. With the deepening of genomics research, ethical and privacy issues become increasingly important. Genome prediction involves a large amount of highly sensitive genetic information, so privacy protection needs to be strengthened during data collection, storage, and sharing. At the same time, how to explain and present the results of genome prediction to individuals, as well as how to apply this information in clinical practice, also requires clearer ethical guidance. It is also important to consider the social impact of genetic information. When conducting genome prediction, it may reveal information related to other diseases, traits, or family history, which may have potential impacts on individuals' employment, insurance, and other aspects. Establishing a sound ethical framework that safeguards individual rights while promoting scientific research is one of the important challenges facing current genome prediction research. Despite the challenges faced by genome prediction in Alzheimer's disease research, there are still many promising future research directions. Personalized genome prediction models will be the focus of future research. Researchers can also further explore the potential value of genome prediction in the early diagnosis and prevention of Alzheimer's disease. Additionally, with the continuous advancement of technology, the widespread application of new technologies such as whole-genome sequencing and single-cell sequencing will provide more abundant data for genome prediction research. The development of these technologies is expected to help researchers more comprehensively and deeply understand the genetic basis of Alzheimer's disease. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Ando K., Nagaraj S., Küçükali F., De Fisenne M.A., Kosa A.C., Doeraene E., and Leroy K., 2022, PICALM and Alzheimer’s disease: an update and perspectives, Cells, 11(24): 3994. https://doi.org/10.3390/cells11243994
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