A Deep Neural Network Model with Attribute Network Representation for lncRNA-Protein Interaction Prediction

Abstract

Background: LncRNA is not only involved in the regulation of the biological functions of protein-coding genes, but its dysfunction is also associated with the occurrence and progression of various diseases. Various studies have shown that an in-depth understanding of the mechanism of action of lncRNA is of great significance for disease treatment. However, traditional wet testing is time-consuming, laborious, expensive, and has many subjective factors which may affect the accuracy of the experiment. Objective: Most of the methods for predicting lncRNA-protein interaction (LPI) rely on a single feature, or there is noise in the feature. To solve this problem, we proposed a computational model, CSALPI based on a deep neural network. Methods: Firstly, this model utilizes cosine similarity to extract similarity features for lncRNAlncRNA and protein-protein, denoising similar features using the Sparse Autoencoder. Second, a neighbor enhancement autoencoder is employed to enforce neighboring nodes to be represented similarly by reconstructing the denoised features. Finally, a Light Gradient Boosting Machine classifier is used to predict potential LPIs. Results: To demonstrate the reliability of CSALPI, multiple evaluation metrics were used under a 5- fold cross-validation experiment, and excellent results were achieved. In the case study, the model successfully predicted 7 out of 10 disease-associated lncRNA and protein pairs. Conclusion: The CSALPI can be an effective complementary method for predicting potential LPIs from biological experiments.