SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.
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 multip...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-12-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1006616 |
| _version_ | 1818716786425593856 |
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| author | Wen Zhang Xiang Yue Guifeng Tang Wenjian Wu Feng Huang Xining Zhang |
| author_facet | Wen Zhang Xiang Yue Guifeng Tang Wenjian Wu Feng Huang Xining Zhang |
| author_sort | Wen Zhang |
| collection | DOAJ |
| description | 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/. |
| first_indexed | 2024-12-17T19:24:48Z |
| format | Article |
| id | doaj.art-9c70d099aae14169b1c5a22cfa972caf |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-17T19:24:48Z |
| publishDate | 2018-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-9c70d099aae14169b1c5a22cfa972caf2022-12-21T21:35:24ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582018-12-011412e100661610.1371/journal.pcbi.1006616SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.Wen ZhangXiang YueGuifeng TangWenjian WuFeng HuangXining ZhangLncRNA-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/.https://doi.org/10.1371/journal.pcbi.1006616 |
| spellingShingle | Wen Zhang Xiang Yue Guifeng Tang Wenjian Wu Feng Huang Xining Zhang SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions. PLoS Computational Biology |
| title | SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions. |
| title_full | SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions. |
| title_fullStr | SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions. |
| title_full_unstemmed | SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions. |
| title_short | SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions. |
| title_sort | sfpel lpi sequence based feature projection ensemble learning for predicting lncrna protein interactions |
| url | https://doi.org/10.1371/journal.pcbi.1006616 |
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