Abstract
Alzheimer’s disease (AD) is the most common form of dementia. However, its pathogenesis is not fully understood, and one of the methods to explore the pathogenesis of AD is to search for causative genes. In this study, we conducted research using AD-related DNA microarray data, learned from earthquakes as an analogy. We considered that earthquakes release varying amounts of energy, causing different levels of impact on the ground. Similarly, we compared this phenomenon to the gene correlations during different stages of the disease. We regarded the energy released by an earthquake as the differences in gene correlations at the disease stage and likened the ground surface damage caused by earthquakes to AD’s pathogenesis. Based on these insights, we developed a Chebyshev inequality screening algorithm that utilizes correlation calculations to identify genes associated with AD. The results showed this approach identified 46 AD candidate genes, most of which are closely associated with AD. Computational validation supports the reliability of the algorithm, providing further possibilities to explore potential molecular mechanisms. This study makes significant contributions to advancing our understanding of AD and offers promising directions for future research and potential therapeutic targets.
L. Yu and X. Tan contributed equally to this work and share first authorship
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Yu, L. et al. (2024). Chebyshev Inequality and the Identification of Genes Associated with Alzheimer’s Disease. In: Pan, JS., Pan, Z., Hu, P., Lin, J.CW. (eds) Genetic and Evolutionary Computing. ICGEC 2023. Lecture Notes in Electrical Engineering, vol 1114. Springer, Singapore. https://doi.org/10.1007/978-981-99-9412-0_10
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DOI: https://doi.org/10.1007/978-981-99-9412-0_10
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