Efficient Framework for Predicting ncRNA-Protein Interactions Based on Sequence Information by Deep Learning

The interactions between proteins and RNA (RPIs) play a crucial role in most cellular processes such as RNA stability and translation. Although there have been many high-throughput experiments recently to detect RPIs, these experiments are largely time-consuming and labor-intensive. Therefore, it is imminent to propose an efficient computational method to predict RPIs. In this study, we put forward a novel approach for predicting protein and ncRNA interactions based on sequences information only. By employing the bi-gram probability feature extraction method and k-mer algorithm, the represent features from protein and ncRNA were extracted. To evaluate the performance of the proposed model, two widely used datasets named RPI1807 and RPI2241 were trained with the adoption of random forest classifier by using five-fold cross-validation. The experimental results with the AUC of 0.992 and 0.947 on dataset RPI1807 and RPI2241 respectively indicated the effectiveness of our experimental approach for predicting RPIs, which provided the guidance for reference for future research in the biological field.