miRNA-disease Association Prediction Model Based on Stacked Autoencoder

Abstract

As a group of small non-coding RNA,the abnormal regulation of miRNA is closely related to the occurrence and deve-lopment of human diseases.The study on the associations between miRNA and disease is important for understanding the pathogenic mechanism of human diseases.Machine learning methods are widely used to predict miRNA-disease associations.However,existing methods only consider the information of miRNA and disease similarity networks,ignoring the topology structure of the similarity networks.Therefore,SAEMDA model based on stacked autoencoder is proposed in this paper,it gets the topological structure features of miRNA and disease similarity networks by restart random walk,obtains the abstract low dimensional features of miRNA and disease by stacked autoencoder,and the low dimensional features are input into deep neural network for miRNA-disease associations prediction.SAEMDA model has achieved great results in 5-fold cross-validation,and it has been validated in cases of colon cancer and lung cancer additionally.As for colon cancer,45 of the top 50 miRNA-disease associations predicted by this model are verified in the database;and in the cases of lung cancer,all the top 50 miRNAs are verified in the database.

Keywords

• mirna-disease associations|similarity networks|topological structure|random walk|stacked autoencoder