Fig. 7: Multi-omics osteoarthritis (OA) risk models and biomarkers. | Nature Communications

Fig. 7: Multi-omics osteoarthritis (OA) risk models and biomarkers.

From: Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Fig. 7

A Top ranked features from omics models in the context of multi-modal clinical features. The top five omics features that appeared important for prediction of OA based on the average marginal SHAP value ranking amongst top 40 predictive features. For ClinSNP, ClinGRS and ClinPath, several sensitivity checks were done for the genetic features including results marked with: (1): GRS obtained with proxy, (2): GRS obtained without proxy, and * Identified for models with genetic features corrected for population stratification (Supplementary Table 3 for details). For the column Gene OA Risk Score, italic refers to the defined gene loci. B Ranked feature importance of ClinGRS (proxy) model by SHAP additive explanations for top 40 predictive features in the model. C Ranked feature importance of ClinPath (proxy) model by SHAP additive explanations for top 40 predictive features in the model. D Ranked feature importance of ClinMet model by SHAP additive explanations for top 40 predictive features in the model. E Ranked feature importance of ClinPro model by SHAP additive explanations for top 40 predictive features in the model. OA Osteoarthritis, NSAIDs non-steroidal anti-inflammatory steroid drugs, FM/FFM Fat mass/Fat-free mass.

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