A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure
Abstract Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA–protein Interactions (LPIs) is significantly important to well characterize the biological functions and mechanisms of lncRNAs. Existing comp...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-98277-1 |
| _version_ | 1818683169166065664 |
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| author | Xiongfei Tian Ling Shen Zhenwu Wang Liqian Zhou Lihong Peng |
| author_facet | Xiongfei Tian Ling Shen Zhenwu Wang Liqian Zhou Lihong Peng |
| author_sort | Xiongfei Tian |
| collection | DOAJ |
| description | Abstract Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA–protein Interactions (LPIs) is significantly important to well characterize the biological functions and mechanisms of lncRNAs. Existing computational methods have been effectively applied to LPI prediction. However, the majority of them were evaluated only on one LPI dataset, thereby resulting in prediction bias. More importantly, part of models did not discover possible LPIs for new lncRNAs (or proteins). In addition, the prediction performance remains limited. To solve with the above problems, in this study, we develop a Deep Forest-based LPI prediction method (LPIDF). First, five LPI datasets are obtained and the corresponding sequence information of lncRNAs and proteins are collected. Second, features of lncRNAs and proteins are constructed based on four-nucleotide composition and BioSeq2vec with encoder-decoder structure, respectively. Finally, a deep forest model with cascade forest structure is developed to find new LPIs. We compare LPIDF with four classical association prediction models based on three fivefold cross validations on lncRNAs, proteins, and LPIs. LPIDF obtains better average AUCs of 0.9012, 0.6937 and 0.9457, and the best average AUPRs of 0.9022, 0.6860, and 0.9382, respectively, for the three CVs, significantly outperforming other methods. The results show that the lncRNA FTX may interact with the protein P35637 and needs further validation. |
| first_indexed | 2024-12-17T10:30:28Z |
| format | Article |
| id | doaj.art-a0acb30f0ee4466cabc1620b344531dd |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-17T10:30:28Z |
| publishDate | 2021-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-a0acb30f0ee4466cabc1620b344531dd2022-12-21T21:52:33ZengNature PortfolioScientific Reports2045-23222021-09-0111111510.1038/s41598-021-98277-1A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structureXiongfei Tian0Ling Shen1Zhenwu Wang2Liqian Zhou3Lihong Peng4School of Computer Science, Hunan University of TechnologySchool of Computer Science, Hunan University of TechnologySchool of Computer Science, Hunan University of TechnologySchool of Computer Science, Hunan University of TechnologySchool of Computer Science, Hunan University of TechnologyAbstract Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA–protein Interactions (LPIs) is significantly important to well characterize the biological functions and mechanisms of lncRNAs. Existing computational methods have been effectively applied to LPI prediction. However, the majority of them were evaluated only on one LPI dataset, thereby resulting in prediction bias. More importantly, part of models did not discover possible LPIs for new lncRNAs (or proteins). In addition, the prediction performance remains limited. To solve with the above problems, in this study, we develop a Deep Forest-based LPI prediction method (LPIDF). First, five LPI datasets are obtained and the corresponding sequence information of lncRNAs and proteins are collected. Second, features of lncRNAs and proteins are constructed based on four-nucleotide composition and BioSeq2vec with encoder-decoder structure, respectively. Finally, a deep forest model with cascade forest structure is developed to find new LPIs. We compare LPIDF with four classical association prediction models based on three fivefold cross validations on lncRNAs, proteins, and LPIs. LPIDF obtains better average AUCs of 0.9012, 0.6937 and 0.9457, and the best average AUPRs of 0.9022, 0.6860, and 0.9382, respectively, for the three CVs, significantly outperforming other methods. The results show that the lncRNA FTX may interact with the protein P35637 and needs further validation.https://doi.org/10.1038/s41598-021-98277-1 |
| spellingShingle | Xiongfei Tian Ling Shen Zhenwu Wang Liqian Zhou Lihong Peng A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure Scientific Reports |
| title | A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure |
| title_full | A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure |
| title_fullStr | A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure |
| title_full_unstemmed | A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure |
| title_short | A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure |
| title_sort | novel lncrna protein interaction prediction method based on deep forest with cascade forest structure |
| url | https://doi.org/10.1038/s41598-021-98277-1 |
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