RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision

• Published in: Mathematical biosciences and engineering : MBE Vol. 19; no. 5; pp. 4749 - 4764
• Main Authors: Song, Jinmiao, Tian, Shengwei, Yu, Long, Yang, Qimeng, Dai, Qiguo, Wang, Yuanxu, Wu, Weidong, Duan, Xiaodong
• Format: Journal Article
• Language: English
• Published: United States AIMS Press 11.03.2022
• Subjects: attention mechanism; Computational Biology - methods; deep learning; extra trees; fuzzy decision; lncrna-protein interaction; Machine Learning; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; fuzzy decision; deep learning; extra trees; attention mechanism; lncRNA-protein interaction
• ISSN: 1551-0018; 1551-0018
• DOI: 10.3934/mbe.2022222

Long non-coding RNAs (lncRNAs) play a regulatory role in many biological cells, and the recognition of lncRNA-protein interactions is helpful to reveal the functional mechanism of lncRNAs. Identification of lncRNA-protein interaction by biological techniques is costly and time-consuming. Here, an ensemble learning framework, RLF-LPI is proposed, to predict lncRNA-protein interactions. The RLF-LPI of the residual LSTM autoencoder module with fusion attention mechanism can extract the potential representation of features and capture the dependencies between sequences and structures by k-mer method. Finally, the relationship between lncRNA and protein is learned through the method of fuzzy decision. The experimental results show that the ACC of RLF-LPI is 0.912 on ATH948 dataset and 0.921 on ZEA22133 dataset. Thus, it is demonstrated that our proposed method performed better in predicting lncRNA-protein interaction than other methods.