Figure 1.

Block diagram of the proposed system. The first step is the preprocessing (log transformation, centering, autoscaling, and quantile normalization). We used Autoencoder pretraining (unsupervised step) to initial model weights and select model architecture. Model used the 80% of data split to train the model and the remaining 20% to measure model performance. The data were split 10 times to avoid the bias of data sampling, and the average AUC was calculated on the 10 hold out test sets.