Fig. 5: Explainability analyses result of the clinic-RadmC model.
From: Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer
SHAP analyses results obtained for ranking the impact of the continuous features included in the clinic-RadmC models on the risk probabilities output for the validation set A, internal test set B, and external test set C; 2 example participants with similar clinic-radiological characteristics in the low-risk D and high-risk subgroups E, respectively. Each participant was represented by a single dot on each feature flow. The horizontal position of the dot was determined by the SHAP value of that feature, and dots were accumulated along each feature row to show density values. DL-radiomics, deep learning-based radiomic model; 6bp-5mC, the model established by the 6-mer end motifs selected from 5mC-sequencing data; solid size, the radiological solid component size of pulmonary nodules; SHAP, Shapley additive explanations. Source data are provided as a Source Data file.
