Fig. 6: Supervised learning to predict COVID-19 severity from molecular features. | Nature Communications

Fig. 6: Supervised learning to predict COVID-19 severity from molecular features.

From: Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence

Fig. 6

a Point estimates (mean) and 95% confidence intervals of the area under the receiver operating characteristic curve (AUC-ROC) for predicting COVID-19 severity (from 200 cross-validation resamples of 51 independent samples) using lasso regression. Both = supervised learning on the combined proteomic and transcriptomic data. b Important proteins (left) and genes (right) for the lasso model. Feature importance is scaled between 0 and 1, where 1 represents the most important feature. c The profile of LRRC15 plasma protein concentration over time, stratified by severity of the patients’ overall clinical course (n = 169 samples from 40 individuals). Left: lines represent estimated LMM marginal means and shaded areas represent their 95% confidence intervals. Right: raw data for each individual.

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