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. 2022 Jan 27;9(1):e001898. doi: 10.1136/openhrt-2021-001898

Figure 2.

Figure 2

Overview of the CNN-GWAS based framework. (A) A previously reported GWAS-based sampling extracted a set of SNPs according to the p value cut-off. (B) Digitisation encoding for heterozygous or homozygous by minor alleles. (C) Process of the CNN-based neural network model prediction and analysis. (D) Neural network architecture based on the CNN. (F) Saliency score analysis of the predicted AF patients. The same AF patients (n=872) were used for the quantitative evaluation of the five-time pretrained models using different samples. AF, atrial fibrillation; CNN, convolutional neural network; Grad-CAM, gradient-weighted class activation mapping; GWAS, Genome-Wide Association Study; SNP, single-nucleotide polymorphism.