Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification approach to identify disease-related lncRNAs by graph auto-encoder (GAE) and random forest (RF) (GAERF). First, we combined the relationship of lncRNA, miRNA and disease into a heterogeneous network. Then, low-dimensional representation vectors of nodes were learned from the network by GAE, which reduce the dimension and heterogeneity of biological data. Taking these feature vectors as input, we trained a RF classifier to predict new lncRNA-disease associations (LDAs). Related experiment results show that the proposed method for the representation of lncRNA-disease characterizes them accurately. GAERF achieves superior performance owing to the ensemble learning method, outperforming other methods significantly. Moreover, case studies further demonstrated that GAERF is an effective method to predict LDAs.
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Article Synopsis
- Long non-coding RNAs (lncRNAs) play a significant role in handling complex human diseases, making it vital to predict their associations with diseases efficiently.
- The paper introduces a new prediction method called LDAGM that utilizes a Graph Convolutional Autoencoder and Multilayer Perceptron to analyze various similarities and construct networks, capturing deep connections between lncRNAs and diseases.
- The method's effectiveness was confirmed through parameter analysis and various experiments, indicating it enhances prediction accuracy for lncRNA-disease associations.
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