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...

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Autores principales:	Xiongfei Tian, Ling Shen, Zhenwu Wang, Liqian Zhou, Lihong Peng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a0acb30f0ee4466cabc1620b344531dd
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spelling	oai:doaj.org-article:a0acb30f0ee4466cabc1620b344531dd2021-12-02T18:14:22ZA novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure10.1038/s41598-021-98277-12045-2322https://doaj.org/article/a0acb30f0ee4466cabc1620b344531dd2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98277-1https://doaj.org/toc/2045-2322Abstract 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.Xiongfei TianLing ShenZhenwu WangLiqian ZhouLihong PengNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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
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.
format	article
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
title	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_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_sort	novel lncrna–protein interaction prediction method based on deep forest with cascade forest structure
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a0acb30f0ee4466cabc1620b344531dd
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A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure

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