Fig. 5: Performance of parsimonious multi-omic models and analyte contribution for disease survival. | Nature Cancer

Fig. 5: Performance of parsimonious multi-omic models and analyte contribution for disease survival.

From: The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients

Fig. 5

a, Parsimonious model of all multi-omic features and full dataset. The blue dotted line box indicates the parsimonious model at the inflection point. b, Clinical and surgical pathology and computational pathology analytes only. c, All plasma analytes (lipidomics and protein) only. d, All clinical and surgical pathology, computational pathology and plasma analytes (lipidomics and protein) only. Left y axis shows accuracy and PPV score: multi-omic model performance across feature reduction steps by restricting the maximum selectable features during model training. The x axis shows the number of maximum features at each reduction step. The right y axis shows the analyte percent (%) contribution: each analyte’s aggregated absolute feature weight contribution at each feature reduction step.

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