Figure 3

Classification performance of biomarker panels. Different machine learning models were trained in one blood dataset (GSE63060 or GSE63061) and tested in the other (GSE63061 or GSE63060). Results shown from the different ML models in (a,c) all use the same panel of six features (panel Full6set), while ML models in (b,d) use one with four features (panel Full4set). Full6set contains six probesets, i.e. ILMN_2097421 (MRPL51), ILMN_2189933 (RPL36AL), ILMN_1695645 (CETN2), ILMN_1703617 (AHSA1), ILMN_2237746 (ING3), and ILMN_1939297 (GALNT4). Full4set contains four probesets: ILMN_1784286 (NDUFA1), ILMN_2097421 (MRPL51), ILMN_2189933 (RPL36AL) and ILMN_2189936 (RPL36AL). The AUC of vote is the average testing AUCs of SVM, RR, and RF models. See Supplementary Table 6 for detailed performance.