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. Author manuscript; available in PMC: 2019 Mar 15.
Published in final edited form as: Clin Cancer Res. 2017 Oct 5;24(6):1248–1259. doi: 10.1158/1078-0432.CCR-17-0853

Figure 1. Overall workflow.

Figure 1

(A) Autoencoder architecture used to integrate 3 omics of HCC data. (B) Workflow combining deep learning and machine learning techniques to predict HCC survival subgroups. The workflow includes two steps. Step 1: inferring survival subgroups and Step 2: predicting risk labels for new samples. In step 1: mRNA, DNA methylation and miRNA features from TCGA HCC cohort are stacked up as input features for autoencoder, a deep learning method; then each of the new, transformed features in the bottle neck layer of autoencoder is then subject to single variate Cox-PH models, to select the features associated with survival; then K-mean clustering is applied to samples represented by these features, to identify survival-risk groups. In step 2, mRNA, methylation and miRNA input features are ranked by ANOVA test F-values, those features that are in common with the predicting dataset are selected, then top features are used to build SVM model(s) to predict the survival risk labels of new datasets.