Prediction of plant LncRNA-protein interactions based on feature fusion and an improved residual network

• Published in: Expert systems with applications Vol. 238; p. 121991
• Main Authors: Zhang, Lina, Yang, Runtao, Xia, Defei, Lin, Xiaorui, Xiong, Wanying
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.03.2024
• Subjects: Attention mechanism; LncRNA-protein interaction; Long short-term memory; Residual network; Long short-term memory; Attention mechanism; LncRNA-protein interaction; Residual network
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2023.121991

LncRNA(long non-coding RNA)-protein interaction (LPI) has effects on chromosome structure and gene transcription, participating in key cellular processes such as signal transduction, chromosome replication, material transport, and mitosis. Accurate identification of LPIs will provide scientific basis for understanding the molecular mechanisms of LncRNA-related diseases, thereby promoting the progress of disease diagnosis technologies and the development of therapeutic procedures to a certain extent. How to comprehensively mine feature information reflecting functional attributes from LncRNAs and proteins, apply deep learning to extract advanced features from the original input features of proteins and LncRNAs, and effectively fuse them have been important research challenges in this field. Aiming at the limitations of existing methods, based on deep learning techniques such as bi-directional long short-term memory (BiLSTM), attention mechanism, and an improved residual network, an LPI prediction model called LPI-LSTM-ResNet is constructed in this paper. Firstly, the sequence and structural information of LncRNAs and proteins are extracted from different perspectives. Then, the deep interaction fused features between LncRNAs and proteins are obtained by BiLSTM and attention mechanism. Finally, the fused features are input into an improved residual network with LSTM as the residual element to preserve long-distance dependencies between sequences. The 5-fold cross-validation results indicate that the feature combination strategy, feature fusion strategy, and the improved residual network consistently improve the LPI prediction performance. Compared with existing methods on the same plant datasets, LPI-LSTM-ResNet exhibits superior performance. •Sequence and structural information of LncRNAs and proteins are extracted.•Deep interaction fused features are obtained by BiLSTM and attention mechanism.•An improved residual network is proposed to preserve long-distance dependencies.•Compared with existing methods, LPI-LSTM-ResNet exhibits superior performance.