An adversarial deep neural network for identifying non-alcoholic fatty liver disease

Background Non-alcoholic fatty liver disease (NAFLD) is rapidly becoming the most prevalent liver disease worldwide. The accurate identification of NAFLD patients and understand the molecular mechanism of initiating and driving disease progression remain limited. Objectie This study aims to develop a robust and accurate computational model for identifying NAFLD patients using mRNA expression profiles, and to explore the underlying molecular mechanisms that drive disease initiation and progression. Methods We introduce an adversarial deep neural network (ADNN) to identify NAFLD patients based on mRNA expression profiles, which uses adversarial learning techniques to take advantage of existing mRNA expression profiles in the GEO database. We compare the ADNN model with some traditional machine learning methods, such as random forest (RF), adaptive boosting (AdaBoost). Results The area under the receiver operating characteristic curve (AUROC) of the ADNN model is 0.982, which is much higher than the machine learning models such as RF, SVM and deep neural networks. Meanwhile, by combining the ADNN model with the DeepLIFT algorithm, we calculate the importance score of genes and find that the DeepLIFT algorithm can identify genes that don’t differ at the level of mRNA, but interact with other important genes. Such as GSTM1, with the greatest importance score and has no difference between control and NAFLD, but interacts with IGF1 and MEP1B. Conclusion Therefore, the ADNN model can not only identify NAFLD patients from healthy controls, but also help us better understand the disease mechanism of NAFLD.