Fig. 6: A BINN trained and constructed from Olink-data.
To demonstrate the ability of the BINN package to generalize cross-platform, a BINN was constructed using an proteomic dataset generated using the Olink platform and the Reactome pathway database31. The Olink-BINN was trained to differentiate between COVID-19-induced ARDS, bacterial sepsis-induced ARDS and healthy controls. a The resulting BINN. Since this is a three-class classification, the connections are colored based on which class the SHAP value pertains to. The flow is partitioned into the three classes, allowing us to identify which nodes are important for classifying a particular class. For example, Neutrophil degranulation is important during the classification of bacterial sepsis-induced ARDS and healthy controls, whereas Post translatuional protein phosphorylation is mostly important for COVID-19-induced ARDS. The node colors still reflect the mean importance and the nodes are ordered accordingly. b The average f1-score, precision and recall of the Olink-BINN for the different classes during validation with k-fold cross validation (k = 3). Error bars show the 95% confidence interval. c Confusion matrix averaged across folds and normalized per true label (rows sum to 100%). The mean and 95% confidence interval is annotated in the matrix. Source data for all panels are provided as a Source Data file.
