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SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.
In: PLoS Computational Biology, Jg. 14 (2018-12-11), Heft 12, S. 1-21
Online
academicJournal
Zugriff:
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 . [ABSTRACT FROM AUTHOR]
Titel: |
SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.
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Autor/in / Beteiligte Person: | Zhang, Wen ; Tang, Guifeng ; Huang, Feng ; Zhang, Xining ; Yue, Xiang ; Wu, Wenjian |
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Zeitschrift: | PLoS Computational Biology, Jg. 14 (2018-12-11), Heft 12, S. 1-21 |
Veröffentlichung: | 2018 |
Medientyp: | academicJournal |
ISSN: | 1553-734X (print) |
DOI: | 10.1371/journal.pcbi.1006616 |
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