Abstract
Imaging genomics is an effective tool in the detection and causative factor analysis of Alzheimer's disease (AD). Based on single nucleotide polymorphism (SNP) and structural magnetic resonance imaging (MRI) data, we propose a novel AD analysis model using multi-modal data fusion and multi-detection model fusion. Firstly, based on the characteristics of SNP data, we fully extract highly representative features. Secondly, the fused data are used to construct fusion features. Finally, according to the characteristics of the fused data, we design a fused prediction model with multiple machine learning models. The model integrates four machine learning models and feeds them into the final XGBoost model for AD prediction. The experimental results show that the data fusion method and the fusion model of multiple machine learning models proposed in this paper can effectively improve the prediction accuracy. Our model can identify AD patients and detect abnormal brain regions and risk SNPs associated with AD, which can perform the association of risk SNPs with abnormal brain regions.
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Li, Y., Liu, Y., Wang, T., Lei, B. (2021). Alzheimer's Disease Prediction via the Association of Single Nucleotide Polymorphism with Brain Regions. In: Feng, J., Zhang, J., Liu, M., Fang, Y. (eds) Biometric Recognition. CCBR 2021. Lecture Notes in Computer Science(), vol 12878. Springer, Cham. https://doi.org/10.1007/978-3-030-86608-2_12
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DOI: https://doi.org/10.1007/978-3-030-86608-2_12
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