Abstract
To unravel complex genetic mechanisms in genetics, sparse multi-view canonical correlation analysis (SMCCA) and its variants have been widely used to identify associations between the structure or function of the brain and genetic variants to explore the pathogenesis of Alzheimer’s disease (AD). However, many methods pay too much attention to the association between different subjects, ignore the mutual dependence among different subjects. Meanwhile, it is also a challenge to find high-order correlations among multimodal data. In this manuscript, hypergraph and graph guided pairwise group lasso (GGL) robustness-aware adaptive SMCCA (HGRSMCCA) and uncertainty-aware adaptive SMCCA (HGUSMCCA) methods are proposed, to reduce the deviation of data projection and analyze the complex higher-order relationship between genetics and neuroimaging biomarkers. Four state-of-the-art SMCCA methods are compared with HGRSMCCA and HGUSMCCA methods on Alzheimer’s Disease Neuroimaging Initiative dataset. The results showed that our methods not only achieved the highest correlation, but also found more and deeper interest regions related to AD than benchmark methods.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant No. 62172254.
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Wang, YM., Kong, XZ., Guan, BX., Zheng, CH., Gao, YL. (2023). Identify Complex Higher-Order Associations Between Alzheimer’s Disease Genes and Imaging Markers Through Improved Adaptive Sparse Multi-view Canonical Correlation Analysis. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_28
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DOI: https://doi.org/10.1007/978-981-99-4749-2_28
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