RPI-CapsuleGAN: Predicting RNA-protein interactions through an interpretable generative adversarial capsule network

Yifei Wang, Xue Wang, Cheng Chen, Hongli Gao, Adil Salhi, Xin Gao, Bin Yu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

RNA-protein interactions (RPI) play a crucial regulatory role in cellular physiological processes. The study and prediction of RPIs can be insightful for exploring disease mechanisms and drug target design. Traditional RPI prediction methods relied mainly on tedious and expensive biological experiments, and there is an increasing interest in developing more cost-effective computational methods to predict RPIs. This work proposes an interpretable RPI-CapsuleGAN method for RPI prediction based on a generative adversarial capsule network with a convolutional block attention module. First, RPI-CapsuleGAN extracts and fuses multiple features to characterize RNA and protein sequences. Subsequently, the elastic net feature selection method is used to retain features that are highly informative to RPI prediction. Finally, we introduce a convolutional attention mechanism into the generative adversarial capsule network for the first time in order to construct the RPI prediction framework, which is shown to improve the model feature learning of interpretable and expression ability, and effectively solves the problem of the disappearance of the model spatial structure hierarchy. Based on a five-fold cross-validation test, the prediction accuracy of the RPI-CapsuleGAN method reaches 97.1%, 88.8%, 92.5%, 97.3%, and 87.8% for datasets RPI488, RPI369, RPI2241, RPI1807, and RPI1446. The RPI-CapsuleGAN method has higher accuracy than state-of-the-art RPI prediction methods that use the same datasets. In the test dataset NPInter227 constructed in this paper, five groups of test sets are composed of positive samples and five groups of negative samples, the prediction accuracy reaches 97.38%, 96.48%, 97.38%, 97.81%, and 97.15%, respectively, outperforming other mainstream deep learning algorithms. In addition, RPI-CapsuleGAN obtained better results for the prediction of independent test datasets. Extensive experiments detailed here show that RPI-CapsuleGAN can provide an efficient, accurate, and stable method for RPI prediction.
Original languageEnglish (US)
Pages (from-to)109626
JournalPattern Recognition
Volume141
DOIs
StatePublished - May 6 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-05-18
Acknowledged KAUST grant number(s): FCC/1/1976–44–01, FCC/1/1976–45–01, URF/1/4379–01–01, REI/1/4742–01–01
Acknowledgements: This work was supported by the National Natural Science Foundation of China (No. 62172248), the Natural Science Foundation of Shandong Province of China (No. ZR2021MF098), and the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under (FCC/1/1976–44–01, FCC/1/1976–45–01, URF/1/4379–01–01, and REI/1/4742–01–01).

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Software
  • Computer Vision and Pattern Recognition

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