LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification

Abstract Background Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consum...

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Autores principales:	Liqian Zhou, Qi Duan, Xiongfei Tian, He Xu, Jianxin Tang, Lihong Peng
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	C-SVM Deep neural network Ensemble learning Feature selection lncRNA-protein interaction XGBoost Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/433182be94124af091c9a52af13c127c
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spelling	oai:doaj.org-article:433182be94124af091c9a52af13c127c2021-11-28T12:11:14ZLPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification10.1186/s12859-021-04485-x1471-2105https://doaj.org/article/433182be94124af091c9a52af13c127c2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04485-xhttps://doaj.org/toc/1471-2105Abstract Background Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate. Results Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717. Conclusions Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins.Liqian ZhouQi DuanXiongfei TianHe XuJianxin TangLihong PengBMCarticleC-SVMDeep neural networkEnsemble learningFeature selectionlncRNA-protein interactionXGBoostComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-31 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	C-SVM Deep neural network Ensemble learning Feature selection lncRNA-protein interaction XGBoost Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	C-SVM Deep neural network Ensemble learning Feature selection lncRNA-protein interaction XGBoost Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Liqian Zhou Qi Duan Xiongfei Tian He Xu Jianxin Tang Lihong Peng LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification
description	Abstract Background Long noncoding RNAs (lncRNAs) have dense linkages with a plethora of important cellular activities. lncRNAs exert functions by linking with corresponding RNA-binding proteins. Since experimental techniques to detect lncRNA-protein interactions (LPIs) are laborious and time-consuming, a few computational methods have been reported for LPI prediction. However, computation-based LPI identification methods have the following limitations: (1) Most methods were evaluated on a single dataset, and researchers may thus fail to measure their generalization ability. (2) The majority of methods were validated under cross validation on lncRNA-protein pairs, did not investigate the performance under other cross validations, especially for cross validation on independent lncRNAs and independent proteins. (3) lncRNAs and proteins have abundant biological information, how to select informative features need to further investigate. Results Under a hybrid framework (LPI-HyADBS) integrating feature selection based on AdaBoost, and classification models including deep neural network (DNN), extreme gradient Boost (XGBoost), and SVM with a penalty Coefficient of misclassification (C-SVM), this work focuses on finding new LPIs. First, five datasets are arranged. Each dataset contains lncRNA sequences, protein sequences, and an LPI network. Second, biological features of lncRNAs and proteins are acquired based on Pyfeat. Third, the obtained features of lncRNAs and proteins are selected based on AdaBoost and concatenated to depict each LPI sample. Fourth, DNN, XGBoost, and C-SVM are used to classify lncRNA-protein pairs based on the concatenated features. Finally, a hybrid framework is developed to integrate the classification results from the above three classifiers. LPI-HyADBS is compared to six classical LPI prediction approaches (LPI-SKF, LPI-NRLMF, Capsule-LPI, LPI-CNNCP, LPLNP, and LPBNI) on five datasets under 5-fold cross validations on lncRNAs, proteins, lncRNA-protein pairs, and independent lncRNAs and independent proteins. The results show LPI-HyADBS has the best LPI prediction performance under four different cross validations. In particular, LPI-HyADBS obtains better classification ability than other six approaches under the constructed independent dataset. Case analyses suggest that there is relevance between ZNF667-AS1 and Q15717. Conclusions Integrating feature selection approach based on AdaBoost, three classification techniques including DNN, XGBoost, and C-SVM, this work develops a hybrid framework to identify new linkages between lncRNAs and proteins.
format	article
author	Liqian Zhou Qi Duan Xiongfei Tian He Xu Jianxin Tang Lihong Peng
author_facet	Liqian Zhou Qi Duan Xiongfei Tian He Xu Jianxin Tang Lihong Peng
author_sort	Liqian Zhou
title	LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification
title_short	LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification
title_full	LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification
title_fullStr	LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification
title_full_unstemmed	LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification
title_sort	lpi-hyadbs: a hybrid framework for lncrna-protein interaction prediction integrating feature selection and classification
publisher	BMC
publishDate	2021
url	https://doaj.org/article/433182be94124af091c9a52af13c127c
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LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification

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