Abstract: RNA base interactions play an important role in maintaining the stability of its three-dimensional structure,and accurate prediction of base interactions can help predict the three-dimensional structure of RNA.However,due to the small amount of data,the model could not effectively learn the feature distribution of the training data,and existing data characteristics(symmetry and class imbalance) affect the performance of the RNA base interactions prediction model.Aiming at the problems of insufficient model learning and data characteristics,a high-performance RNA base interactions prediction method called tpRNA is proposed based on deep learning.tpRNA introduces transfer learning in RNA base interactions prediction task to weak the influence of insufficient learning in the training process due to the small amount of data,and an efficient loss function and feature extraction module is proposed to give full play to the advantages of transfer learning and convolutional neural network in feature learning to alleviate the problem of data characteristics.Results show that transfer learning can reduce the model deviation caused by less data,the proposed loss function can optimize the model training,and the feature extraction module can extract more effective features.Compared with the state-of-the-art method,tpRNA also has significant advantages in the case of low-quality input features.

Key words: RNA base interactions, Transfer learning, Data characteristic, Loss function, Convolutional neural networks