RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision

Jinmiao Song; Shengwei Tian; Long Yu; Qimeng Yang; Qiguo Dai; Yuanxu Wang; Weidong Wu; Xiaodong Duan; Jinmiao Song; Shengwei Tian; Long Yu; Qimeng Yang; Qiguo Dai; Yuanxu Wang; Weidong Wu; Xiaodong Duan

doi:10.3934/mbe.2022222

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 5: 4749-4764. doi: 10.3934/mbe.2022222

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Research article

RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision

1.
Department of Information Science and Engineering, Xinjiang University, Urumqi 830008, China
2.
Key Laboratory of Big Data Applied Technology, State Ethnic Affairs Commission, Dalian Minzu University, Dalian 116600, China
3.
Department of Software, Xinjiang University, Urumqi 830008, China
4.
Key Laboratory of Signal and Information Processing, Xinjiang University, Urumqi 830008, China
5.
Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi 830008, China
6.
Center for Science Education, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830001, China

Academic Editor: Vladimir Mityushev

Received: 27 November 2021 Revised: 28 February 2022 Accepted: 02 March 2022 Published: 11 March 2022

Long non-coding RNAs (lncRNAs) play a regulatory role in many biological cells, and the recognition of lncRNA-protein interactions is helpful to reveal the functional mechanism of lncRNAs. Identification of lncRNA-protein interaction by biological techniques is costly and time-consuming. Here, an ensemble learning framework, RLF-LPI is proposed, to predict lncRNA-protein interactions. The RLF-LPI of the residual LSTM autoencoder module with fusion attention mechanism can extract the potential representation of features and capture the dependencies between sequences and structures by k-mer method. Finally, the relationship between lncRNA and protein is learned through the method of fuzzy decision. The experimental results show that the ACC of RLF-LPI is 0.912 on ATH948 dataset and 0.921 on ZEA22133 dataset. Thus, it is demonstrated that our proposed method performed better in predicting lncRNA-protein interaction than other methods.
- deep learning,
- lncRNA-protein interaction,
- fuzzy decision,
- extra trees,
- attention mechanism
Citation: Jinmiao Song, Shengwei Tian, Long Yu, Qimeng Yang, Qiguo Dai, Yuanxu Wang, Weidong Wu, Xiaodong Duan. RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision[J]. Mathematical Biosciences and Engineering, 2022, 19(5): 4749-4764. doi: 10.3934/mbe.2022222

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Abstract

Long non-coding RNAs (lncRNAs) play a regulatory role in many biological cells, and the recognition of lncRNA-protein interactions is helpful to reveal the functional mechanism of lncRNAs. Identification of lncRNA-protein interaction by biological techniques is costly and time-consuming. Here, an ensemble learning framework, RLF-LPI is proposed, to predict lncRNA-protein interactions. The RLF-LPI of the residual LSTM autoencoder module with fusion attention mechanism can extract the potential representation of features and capture the dependencies between sequences and structures by k-mer method. Finally, the relationship between lncRNA and protein is learned through the method of fuzzy decision. The experimental results show that the ACC of RLF-LPI is 0.912 on ATH948 dataset and 0.921 on ZEA22133 dataset. Thus, it is demonstrated that our proposed method performed better in predicting lncRNA-protein interaction than other methods.

References

[1]	D. Guan, W. Zhang, G. H. Liu, J. C. Belmonte, Switching cell fate, ncRNAs coming to play, Cell Death Dis., 4 (2013), e464. https://doi.org/10.1038/cddis.2012.196 doi: 10.1038/cddis.2012.196
[2]	J. J. Quinn, H. Y. Chang, Unique features of long non-coding RNA biogenesis and function, Nat. Rev. Genet., 17 (2016), 47–62. https://doi.org/10.1038/nrg.2015.10 doi: 10.1038/nrg.2015.10
[3]	K. Panzitt, M. M. O. Tschernatsch, C. Guelly, T. Moustafa, M. Stradner, H. M. Strohmaier, et al., Characterization of HULC, a novel gene with striking up-regulation in hepatocellular carcinoma, as noncoding RNA, Gastroenterology, 132 (2007), 330–342. https://doi.org/10.1053/j.gastro.2006.08.026 doi: 10.1053/j.gastro.2006.08.026
[4]	J. Wang, X. Liu, H. Wu, P. Ni, Z. Gu, Y. Qiao, et al., CREB up-regulates long non-coding RNA, HULC expression through interaction with microRNA-372 in liver cancer, Nucleic Acids Res., 38 (2010), 5366–5383. https://doi.org/10.1093/nar/gkq285 doi: 10.1093/nar/gkq285
[5]	A. C. Kaushik, A. Mehmood, X. Wang, D. Q. Wei, X. Dai, Globally ncrnas expression profiling of tnbc and screening of functional lncrna, Front. Bioeng. Biotechnol., 8 (2021), 1480. https://doi.org/10.3389/fbioe.2020.523127 doi: 10.3389/fbioe.2020.523127
[6]	X. Pan, P. Rijnbeek, J. Yan, H. B. Shen, Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks, BMC Genomics, 19 (2018). https://doi.org/10.1186/s12864-018-4889-1
[7]	D. Adjeroh, M. Allaga, J. Tan, J. Lin, Y. Jiang, A. Abbasi, et al., Feature-based and string-based models for predicting RNA-protein interaction, Molecules, 23 (2018), 697. https://doi.org/10.3390/molecules23030697 doi: 10.3390/molecules23030697
[8]	S. W. Zhang, X. N. Fan, Computational methods for predicting ncRNA-protein interactions, Med. Chem., 13 (2017), 515–525. https://doi.org/10.2174/1573406413666170510102405 doi: 10.2174/1573406413666170510102405
[9]	L. Peng, F. Liu, J. Yang, X. Liu, Y. Meng, X. Deng, et al., Probing lncRNA–protein interactions: data repositories, models, and algorithms, Front. Genet., (2020), 1346. https://doi.org/10.3389/fgene.2019.01346
[10]	H. Hu, L. Zhang, H. Ai, H. Zhang, Y. Fan, Q. Zhao, H Liu, et al., HLPI-Ensemble: Prediction of human lncRNA-protein interactions based on ensemble strategy, RNA Biol., 15 (2018), 797–806. https://doi.org/10.1080/15476286.2018.1457935 doi: 10.1080/15476286.2018.1457935
[11]	Q. Lu, S. Ren, M. Lu, Y. Zhang, D. Zhu, X. Zhang, et al., Computational prediction of associations between long non-coding RNAs and proteins, BMC Genomics, 14 (2013). https://doi.org/10.1186/1471-2164-14-651
[12]	W. Zhang, Q. Qu, Y. Zhang, W. Wang, The linear neighborhood propagation method for predicting long non-coding RNA–protein interactions, Neurocomputing, 273 (2018), 526–534. https://doi.org/10.1016/j.neucom.2017.07.065 doi: 10.1016/j.neucom.2017.07.065
[13]	Q. Zhao, Y. Zhang, H. Hu, G. Ren, W. Zhang, H. Liu, IRWNRLPI: integrating random walk and neighborhood regularized logistic matrix factorization for lncRNA-protein interaction prediction, Front. Genet., 9 (2018), 239. https://doi.org/10.3389/fgene.2018.00239 doi: 10.3389/fgene.2018.00239
[14]	R. Zhu, G. Li, J. X. Liu, L. Y. Dai, Y. Guo, ACCBN: Ant-Colony-clustering-based bipartite network method for predicting long non-coding RNA-protein interactions, BMC Bioinf., 20 (2019). https://doi.org/10.1186/s12859-018-2586-3
[15]	T. Zhang, M. Wang, J. Xi, A. Li, LPGNMF: predicting long non-coding RNA and protein interaction using graph regularized nonnegative matrix factorization, IEEE/ACM Trans. Comput. Biol. Bioinf., 17 (2018), 189–197. https://doi.org/10.1109/TCBB.2018.2861009 doi: 10.1109/TCBB.2018.2861009
[16]	H. Zhang, Z. Ming, C. Fan, Q. Zhao, H. Liu, A path-based computational model for long non-coding RNA-protein interaction prediction, Genomics, 112 (2020), 1754–1760. https://doi.org/10.1016/j.ygeno.2019.09.018 doi: 10.1016/j.ygeno.2019.09.018
[17]	U. K. Muppirala, V. G. Honavar, D. Dobbs, Predicting RNA-protein interactions using only sequence information, BMC Bioinf., 12 (2011). https://doi.org/10.1186/1471-2105-12-489
[18]	Y. Wang, X. Chen, Z. P. Liu, Q. Huang, Y. Wang, D. Xu, et al., De novo prediction of RNA-protein interactions from sequence information, Mol. Biosyst., 9 (2013), 133–142. https://doi.org/10.1039/C2MB25292A doi: 10.1039/C2MB25292A
[19]	X. Pan, Y. X. Fan, J. Yan, H. B. Shen, IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction, BMC Genomics, 17 (2016), 582. https://doi.org/10.1186/s12864-016-2931-8 doi: 10.1186/s12864-016-2931-8
[20]	L. Peng, R. Yuan, L. Shen, P. Gao, L. Zhou, LPI-EnEDT: an ensemble framework with extra tree and decision tree classifiers for imbalanced lncRNA-protein interaction data classification, Biodata Min., 14 (2021), 50. https://orcid.org/0000-0002-2321-3901
[21]	C. Peng, S. Han, H. Zhang, Y. Li, RPITER: a hierarchical deep learning framework for ncRNA–protein interaction prediction, Int. J. Mol. Sci., 20 (2019), 1070. https://doi.org/10.3390/ijms20051070 doi: 10.3390/ijms20051070
[22]	J. S. Wekesa, J. Meng, Y. Luan, A deep learning model for plant lncRNA-protein interaction prediction with graph attention, Mol. Genet. Genomics, 295 (2020), 1091–1102. https://doi.org/10.1007/s00438-020-01682-w doi: 10.1007/s00438-020-01682-w
[23]	J. S. Wekesa, J. Meng, Y. Luan, Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction, Genomics, 112 (2020), 2928–2936. https://doi.org/10.1016/j.ygeno.2020.05.005 doi: 10.1016/j.ygeno.2020.05.005
[24]	H. Zhou, Y. Luan, J. S. Wekesa, J. Meng, Prediction of plant lncRNA-protein interactions using sequence information based on deep learning, in International Conference on Intelligent Computing, (2019), 358–368. https://doi.org/10.1007/978-3-030-26766-7_33
[25]	Y. Huang, B. Niu, Y. Gao, L. Fu, W. Li, CD-HIT Suite: a web server for clustering and comparing biological sequences, Bioinformatics, 26 (2010), 680–682. https://doi.org/10.1093/bioinformatics/btq003 doi: 10.1093/bioinformatics/btq003
[26]	I. Goodfellow, Y. Bengio, A. Courville, Regularization for deep learning, Deep learn., (2016), 216–261.
[27]	Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, E. Hovy, Hierarchical attention networks for document classification, in Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, (2016), 1480–1489. https://doi.org/10.18653/v1/N16-1174
[28]	Q. Kang, J. Meng, J. Cui, Y. Luan, M. Chen, PmliPred: a method based on hybrid model and fuzzy decision for plant miRNA–lncRNA interaction prediction, Bioinformatics, 36 (2020), 2986–2992. https://doi.org/10.1093/bioinformatics/btaa074 doi: 10.1093/bioinformatics/btaa074
[29]	R. Lorenz, S. H. Bernhart, C. H. Siederdissen, H. Tafer, C. Flamm, P. F. Stadler, et al., ViennaRNA Package 2.0, Algorithms Mol. Biol., 6 (2011). https://doi.org/10.1186/1748-7188-6-26
[30]	C. Geourjon, G. Deleage, SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments, Bioinformatics, 11 (1995), 681–684. https://doi.org/10.1093/bioinformatics/11.6.681 doi: 10.1093/bioinformatics/11.6.681
[31]	G. Montavon, G. Orr, K. R. Müller, Neural Networks: Tricks of the Trade, springer, 2012.
[32]	N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15 (2014), 1929–1958.
[33]	J. S. Wekesa, Y. Luan, J. Meng, LPI-DL: A recurrent deep learning model for plant lncRNA-protein interaction and function prediction with feature optimization, in 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), (2020), 499–502. https://doi.org/10.1109/BIBM49941.2020.9313431
[34]	H. C. Yi, Z. H. You, D. S. Huang, X. Li, T. H. Jiang, L. P. Li, A deep learning framework for robust and accurate prediction of ncRNA-protein interactions using evolutionary information, Mol. Ther.-Nucleic Acids, 11 (2019), 337–344. https://doi.org/10.1016/j.omtn.2018.03.001 doi: 10.1016/j.omtn.2018.03.001
[35]	Z. H. Zhan, L. N. Jia, Y. Zhou, L. P. Li, H. C. Yi, BGFE: a deep learning model for ncRNA-protein interaction predictions based on improved sequence information, Int. J. Mol. Sci., 20 (2019), 978. https://doi.org/10.3390/ijms20040978 doi: 10.3390/ijms20040978
[36]	H. C. Yi, Z. H. You, M. N. Wang, Z. H. Guo, Y. B. Wang, J. R. Zhou, RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information, BMC Bioinf., 21 (2020), 60. https://doi.org/10.1186/s12859-020-3406-0 doi: 10.1186/s12859-020-3406-0
[37]	Q. Zhao, H. Yu, Z. Ming, H. Hu, G. Ren, H. Liu, The bipartite network projection-recommended algorithm for predicting long non-coding RNA-protein interactions, Mol. Ther.-Nucleic Acids, 13 (2018), 464–471. https://doi.org/10.1016/j.omtn.2018.09.020 doi: 10.1016/j.omtn.2018.09.020

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