Current BioinformaticsCurrent BioinformaticsCurrent Bioinformatics1574-89362212-392XBentham Science64385710.2174/0115748936267109230919104630Life SciencesResearch ArticleA Deep Neural Network Model with Attribute Network Representation for lncRNA-Protein Interaction PredictionWeiMeng-Menginfo@benthamscience.netYuChang-Qinginfo@benthamscience.netLiLi-Pinginfo@benthamscience.netYouZhu-Honginfo@benthamscience.netLei-Wanginfo@benthamscience.netSchool of Information Engineering, Xijing UniversityCollege of Agriculture and Forestry, Longdong UniversitySchool of Computer Science, Northwestern Polytechnical UniversityGuangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy of Sciences0104202419434135107012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://journals.eco-vector.com/1574-8936/article/view/643857

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.

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