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Fig. 5 | BMC Bioinformatics

Fig. 5

From: SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

Fig. 5

Overview of our proposed SMALF method for predicting miRNA-disease assoications.SMALF consists of four parts: Step1, we decompose miRNA-disease matrix Y into miRNA original feature M and disease original feature \(D^T\). Step2, we utilize stacked autoencoders to learn the latent features of miRNAs and diseases from the original feature M and \(D^T\). Step3,Integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature generates the feature vector representing miRNA-disease. Step4, the XGBoost algorithm is employed to predict the miRNA-disease associations

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