SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions

• Published in: PLoS computational biology Vol. 14; no. 12; p. e1006616
• Main Authors: Zhang, Wen, Yue, Xiang, Tang, Guifeng, Wu, Wenjian, Huang, Feng, Zhang, Xining
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01-12-2018; Public Library of Science (PLoS)
• Subjects: Adenylation; Algorithms; Amino Acid Sequence; Analogies; Base Sequence; Bioinformatics; Biology and life sciences; Classification; Computation; Computational Biology; Computer and Information Sciences; Computer applications; Computer science; Databases, Nucleic Acid; Databases, Protein; Datasets; Ensemble learning; Experiments; Feature extraction; Forecasting; Gene expression; Gene regulation; Humans; Internet; Learning; Machine Learning; Physical Sciences; Post-transcription; Protein Binding - genetics; Protein interaction; Proteins; Research and Analysis Methods; RNA Processing, Post-Transcriptional; RNA, Long Noncoding - chemistry; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; RNA-Binding Proteins - chemistry; RNA-Binding Proteins - genetics; RNA-Binding Proteins - metabolism; RNA-protein interactions; Servers; Splicing; Teaching methods; United States > US; China
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006616

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don't have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, "SFPEL-LPI", to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.