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. As more and more 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) only rely on a single feature or there is noise in the feature. To solve this problem, we propose a computational model CSALPI based on a deep neural network. method: Firstly, this model utilizes cosine similarity to extract similarity features for lncRNA-lncRNA and protein-protein. Denoising similar features using the Sparse Autoencoder. Second, a neighbor enhancement autoencoder is employed to enforce neighboring nodes to be represented in a similar way by reconstructing the denoised features. Finally, a Light Gradient Boosting Machine classifier is used to predict potential LPIs. result: 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 used as an effective complementary method for predicting potential LPIs from biological experiments.