A REPRESENTATION LEARNING MODEL BASED ON VARIATIONAL INFERENCE AND GRAPH AUTOENCODER FOR PREDICTING LNCRNA-DISEASE ASSOCIATIONS

A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations

Abstract Background Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases.Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy.Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still cha

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