Fig. 3

Identification of combinatorial biomarker panel and development of ANN model. a Predictors were selected using 10-fold cross validation. The subjects were systematically rotated between ten folds. Within each fold, differential AAbs were determined comparing HCC patients to controls. The predictors for further model development were generated using the potential biomarkers, which worked in ten folds in the cross validation. b The correlations between any two proteins from the 7 predictors were calculated using all samples (HCC, cirrhotic, and healthy) in the test phase (II). The diagonal indicates the SNR distribution of the sample, the lower left indicates the bivariate scatter plot with a fitted line, and the upper right indicates the correlation coefficient and the significance (*p < 0.05, **p < 0.01, ***p < 0.001). c Schematic representation of the ANN model to predict HCC. Fully connected feedforward neural-networks including 7 input nodes (7 predictors), 5 neurons in the hidden layer, and 2 output nodes were chosen. Back propagation of error algorithm was used as the learning rule, and the average committee vote was used to classify the patient samples