Fig. 2: Performance comparison of predictive models for SNP prioritization.
From: Transformer-based deep learning enhances discovery in migraine GWAS
a Zoomed-in ROC Curves: the Receiver Operating Characteristic (ROC) curves demonstrate the true positive rate (TPR) versus the false positive rate (FPR) for various models, including Ridge Regression, Transformer (InsightGWAS), Neural Networks (DeepGWAS), Logistic Regression, and XGBoost. The Transformer model shows superior performance with a sharper curve indicating higher sensitivity and specificity. b Zoomed-in DET Curves: the Detection Error Tradeoff (DET) curves illustrate the tradeoff between false negative rate (FNR) and false positive rate (FPR) for the same set of models. The Transformer model exhibits the lowest error rates, emphasizing its robustness for SNP prioritization. c t-SNE projections of prediction results from different models. Compared to traditional models, the Transformer model demonstrates a clearer separation between positive (Label 1) and negative samples (Label 0).
