Accurate Prediction of ncRNA-Protein Interactions From the Integration of Sequence and Evolutionary Information

Abstract

Non-coding RNA (ncRNA) plays a crucial role in numerous biological processes including gene expression and post-transcriptional gene regulation. The biological function of ncRNA is mostly realized by binding with related proteins. Therefore, an accurate understanding of interactions between ncRNA and protein has a significant impact on current biological research. The major challenge at this stage is the waste of a great deal of redundant time and resource consumed on classification in traditional interaction pattern prediction methods. Fortunately, an efficient classifier named LightGBM can solve this difficulty of long time consumption. In this study, we employed LightGBM as the integrated classifier and proposed a novel computational model for predicting ncRNA and protein interactions. More specifically, the pseudo-Zernike Moments and singular value decomposition algorithm are employed to extract the discriminative features from protein and ncRNA sequences. On four widely used datasets RPI369, RPI488, RPI1807, and RPI2241, we evaluated the performance of LGBM and obtained an superior performance with AUC of 0.799, 0.914, 0.989, and 0.762, respectively. The experimental results of 10-fold cross-validation shown that the proposed method performs much better than existing methods in predicting ncRNA-protein interaction patterns, which could be used as a useful tool in proteomics research.

Keywords: LightGBM; PSSM; Pseudo-Zernike moments; k-mers; ncRNA-protein interactions.

Figure 1

Step-wise work flow for the purposed LGBM machine learning model.

Figure 2

The ROC curve of dataset RPI369 on three classifiers.

Figure 3

The ROC curve of dataset RPI488 on three classifiers.

Figure 4

The ROC curve of dataset RPI488 on 10-fold cross- validation.

Figure 5

The ROC curve of dataset RPI1807 on 10-fold cross- validation.

Figure 6

The ROC curve of dataset RPI2241 on 10-fold cross- validation.