Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction
Non-coding RNA (ncRNA) plays important roles in many critical regulation processes. Many ncRNAs perform their regulatory functions by the form of RNA-protein complexes. Therefore, identifying the interaction between ncRNA and protein is fundamental to understand functions of ncRNA. Under pressures f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-02-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00018/full |
| _version_ | 1819013518718926848 |
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| author | Qiguo Dai Qiguo Dai Maozu Guo Xiaodong Duan Zhixia Teng Yueyue Fu |
| author_facet | Qiguo Dai Qiguo Dai Maozu Guo Xiaodong Duan Zhixia Teng Yueyue Fu |
| author_sort | Qiguo Dai |
| collection | DOAJ |
| description | Non-coding RNA (ncRNA) plays important roles in many critical regulation processes. Many ncRNAs perform their regulatory functions by the form of RNA-protein complexes. Therefore, identifying the interaction between ncRNA and protein is fundamental to understand functions of ncRNA. Under pressures from expensive cost of experimental techniques, developing an accuracy computational predictive model has become an indispensable way to identify ncRNA-protein interaction. A powerful predicting model of ncRNA-protein interaction needs a good feature set of characterizing the interaction. In this paper, a novel method is put forward to generate complex features for characterizing ncRNA-protein interaction (named CFRP). To obtain a comprehensive description of ncRNA-protein interaction, complex features are generated by non-linear transformations from the traditional k-mer features of ncRNA and protein sequences. To further reduce the dimensions of complex features, a group of discriminative features are selected by random forest. To validate the performances of the proposed method, a series of experiments are carried on several widely-used public datasets. Compared with the traditional k-mer features, the CFRP complex features can boost the performances of ncRNA-protein interaction prediction model. Meanwhile, the CFRP-based prediction model is compared with several state-of-the-art methods, and the results show that the proposed method achieves better performances than the others in term of the evaluation metrics. In conclusion, the complex features generated by CFRP are beneficial for building a powerful predicting model of ncRNA-protein interaction. |
| first_indexed | 2024-12-21T02:01:13Z |
| format | Article |
| id | doaj.art-4694797a98d449468db774cd3a8132e4 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-21T02:01:13Z |
| publishDate | 2019-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-4694797a98d449468db774cd3a8132e42022-12-21T19:19:38ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-02-011010.3389/fgene.2019.00018434778Construction of Complex Features for Computational Predicting ncRNA-Protein InteractionQiguo Dai0Qiguo Dai1Maozu Guo2Xiaodong Duan3Zhixia Teng4Yueyue Fu5School of Computer Science and Engineering, Dalian Minzu University, Dalian, ChinaDalian Key Laboratory of Digital Technology for National Culture, Dalian Minzu University, Dalian, ChinaSchool of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing, ChinaDalian Key Laboratory of Digital Technology for National Culture, Dalian Minzu University, Dalian, ChinaSchool of Information and Computer Engineering, Northeast Forestry University, Harbin, ChinaDepartment of Hematology, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaNon-coding RNA (ncRNA) plays important roles in many critical regulation processes. Many ncRNAs perform their regulatory functions by the form of RNA-protein complexes. Therefore, identifying the interaction between ncRNA and protein is fundamental to understand functions of ncRNA. Under pressures from expensive cost of experimental techniques, developing an accuracy computational predictive model has become an indispensable way to identify ncRNA-protein interaction. A powerful predicting model of ncRNA-protein interaction needs a good feature set of characterizing the interaction. In this paper, a novel method is put forward to generate complex features for characterizing ncRNA-protein interaction (named CFRP). To obtain a comprehensive description of ncRNA-protein interaction, complex features are generated by non-linear transformations from the traditional k-mer features of ncRNA and protein sequences. To further reduce the dimensions of complex features, a group of discriminative features are selected by random forest. To validate the performances of the proposed method, a series of experiments are carried on several widely-used public datasets. Compared with the traditional k-mer features, the CFRP complex features can boost the performances of ncRNA-protein interaction prediction model. Meanwhile, the CFRP-based prediction model is compared with several state-of-the-art methods, and the results show that the proposed method achieves better performances than the others in term of the evaluation metrics. In conclusion, the complex features generated by CFRP are beneficial for building a powerful predicting model of ncRNA-protein interaction.https://www.frontiersin.org/article/10.3389/fgene.2019.00018/fullncRNA-protein interactioncomplex featurefeature constructionfeature selectionrandom forest |
| spellingShingle | Qiguo Dai Qiguo Dai Maozu Guo Xiaodong Duan Zhixia Teng Yueyue Fu Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction Frontiers in Genetics ncRNA-protein interaction complex feature feature construction feature selection random forest |
| title | Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction |
| title_full | Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction |
| title_fullStr | Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction |
| title_full_unstemmed | Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction |
| title_short | Construction of Complex Features for Computational Predicting ncRNA-Protein Interaction |
| title_sort | construction of complex features for computational predicting ncrna protein interaction |
| topic | ncRNA-protein interaction complex feature feature construction feature selection random forest |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.00018/full |
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