Improving RNA Base Interactions Prediction Based on Transfer Learning and Multi-view Feature Fusion

• Published in: Ji suan ji ke xue Vol. 50; no. 3; pp. 164 - 172
• Main Authors: Wang, Xiaofei, Fan, Xueqiang, Li, Zhangwei
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.03.2023; Editorial office of Computer Science
• Subjects: Artificial neural networks; Deep learning; Feature extraction; Machine learning; Modules; Prediction models; rna base interactions|transfer learning|data characteristic|loss function|convolutional neural networks; Structural stability; Training
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.211200186

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