CHANG Zheng,MENG Jun,SHI Yunsheng,et al.LncRNA recognition by fusing multiple features and its function prediction[J].CAAI Transactions on Intelligent Systems,2018,13(06):928-934.[doi:10.11992/tis.201806008]





LncRNA recognition by fusing multiple features and its function prediction
常征 孟军 施云生 莫冯然
大连理工大学 计算机科学与技术学院, 辽宁 大连 116023
CHANG Zheng MENG Jun SHI Yunsheng MO Fengran
School of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China
lncRNAidentificationfeature extractionmultiple features fusionmachine learninginterrelationshipnetwork constructionfunction prediction
Considering the limitations of the traditional plant lncRNA identification based on a single feature, in this paper, a method, in which the open reading frame, secondary structure, and k-mers features of RNA sequences are integrated, is proposed. It involves the training of three classical classification models, Gaussian naive Bayes, support vector machines, and gradient lifting decision tree, and integrating the classification results. The performance of the method was evaluated using cross-validation, and it exhibited superior performance. The accuracy of the proposed method reached 89% when tested with the Arabidopsis thaliana dataset. Using the same dataset, the proposed method outperformed the popular CPAT, CNCI, and PLEK prediction software. In addition, based on the endogenous competition rules and RNA structure information, target prediction and filter rules for lncRNA-microRNA and microRNA-mRNA pairs were executed, and then related tools were used to establish RNA interaction regulatory networks, and the regulatory relationship was analyzed to predict the functions of lncRNAs in modules. Through Gene Ontology term analysis, the possible biological regulation function of lncRNAs can be predicted, and their corresponding functions can be inferred.


[1] COSTA F F. Non-coding RNAs:meet thy masters[J]. Bioassays, 2010, 32(7):599-608.
[2] PALAZZO A F, LEE E S. Non-coding RNA:what is functional and what is junk?[J]. Frontiers in genetics, 2015, 6:Article No.2.
[3] SCHMITZ S U, GROTE P, HERRMANN B G. Mechanisms of long noncoding RNA function in development and disease[J]. Cellular and molecular life sciences, 2016, 73(13):2491-2509.
[4] O’LEARY V B, OVSEPIAN S V, CARRASCOSA L G, et al. PARTICLE, a triplex-forming long ncRNA, regulates locus-specific methylation in response to low-dose irradiation[J]. Cell reports, 2015, 11(3):474-485.
[5] CUI Jun, LUAN Yushi, JIANG Ning, et al. Comparative transcriptome analysis between resistant and susceptible tomato allows the identification of lncRNA16397 conferring resistance to Phytophthora infestans by co-expressing glutaredoxin[J]. The plant journal, 2017, 89(3):577-589.
[6] HAN Siyu, LIANG Yanchun, LI Ying, et al. Long noncoding RNA identification:comparing machine learning based tools for long noncoding transcripts discrimination[J]. BioMed research international, 2016, 2016:Article No.8496165.
[7] KONG Lei, ZHANG Yong, YE Zhiqiang, et al. CPC:assess the protein-coding potential of transcripts using sequence features and support vector machine[J]. Nucleic acids research, 2007, 36(S2):W345-W349.
[8] WANG Liguo, PARK H J, DASARI S, et al. CPAT:coding-potential assessment tool using an alignment-free logistic regression model[J]. Nucleic acids research, 2013, 41(6):Article No.e74.
[9] SUN Liang, LUO Haitao, BU Dechao, et al. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts[J]. Nucleic acids research, 2013, 41(17):Article No.e166.
[10] LI Aimin, ZHANG Junying, ZHOU Zhongyin. PLEK:a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme[J]. BMC bioinformatics, 2014, 15:Article No.311.
[11] 郭杏莉, 高琳, 刘永轩, 等. 长非编码RNA生物特征研究与分析[J]. 科学通报, 2013, 58(27):2779-2786 GUO Xingli, GAO Lin, LIU Yongxuan, et al. Research and analysis of biocharacteristics of long non-coding RNAs[J]. Chinese science bulletin, 2013, 58(27):2779-2786
[12] 李同宇, 李卫军, 覃鸿. 基于特征融合的人脸图像性别识别[J]. 智能系统学报, 2013, 8(6):505-511 LI Tongyu, LI Weijun, QIN Hong. Facial image gender recognition method based on feature fusion[J]. CAAI transactions on intelligent systems, 2013, 8(6):505-511
[13] KARIM S. Exploring plant tolerance to biotic and abiotic stresses[D]. Uppsala, Sweden:Swedish University of Agricultural Sciences, 2007:18-23.
[14] YI Xin, ZHANG Zhenhai, LING Yi, et al. PNRD:a plant non-coding RNA database[J]. Nucleic acids research, 2015, 43(D1):D982-D989.
[15] DINGER M E, PANG K C, MERCER T R, et al. Differentiating protein-coding and noncoding RNA:challenges and ambiguities[J]. PLoS computational biology, 2008, 4(11):Article No.e1000176.
[16] FRITH M C, BAILEY T L, KASUKAWA T, et al. Discrimination of non-protein-coding transcripts from protein-coding mRNA[J]. RNA biology, 2006, 3(1):40-48.
[17] LORENZ R, BERNHART S H, HÖNER ZU SIEDERDISSEN C, et al. ViennaRNA package 2.0[J]. Algorithms for molecular biology, 2011, 6:Article No.26.
[18] 王振武, 孙佳骏, 尹成峰. 改进粒子群算法优化的支持向量机及其应用[J]. 哈尔滨工程大学学报, 2016, 37(12):1728-1733 WANG Zhenwu, SUN Jiajun, YIN Chengfeng. A support vector machine based on an improved particle swarm optimization algorithm and its application[J]. Journal of Harbin engineering university, 2016, 37(12):1728-1733
[19] GRIFFITHS-JONES S, GROCOCK R J, VAN DONGEN S, et al. miRBase:microRNA sequences, targets and gene nomenclature[J]. Nucleic acids research, 2006, 34(S1):D140-D144.
[20] CESANA M, CACCHIARELLI D, LEGNINI I, et al. A long noncoding RNA controls muscle differentiation by functioning as a Competing Endogenous RNA[J]. Cell, 2011, 147(2):358-369.
[21] KRÜGER J, REHMSMEIER M. RNAhybrid:microRNA target prediction easy, fast and flexible[J]. Nucleic acids research, 2006, 34(S2):W451-W454.
[22] WU Huajun, WANG Zhimin, WANG Meng, et al. Widespread long noncoding RNAs as endogenous target mimics for microRNAs in plants[J]. Plant physiology, 2013, 161(4):1875-1884.
[23] SHANNON P, MARKIEL A, OZIER O, et al. Cytoscape:a software environment for integrated models of biomolecular interaction networks[J]. Genome research, 2003, 13(11):2498-2504.
[24] ASHBURNER M, BALL C A, BLAKE J A, et al. Gene ontology:tool for the unification of biology[J]. Nature genetics, 2000, 25(1):25-29.


 LIN Haibo,WANG Hao,ZHANG Yi.Human postures recognition based on the improved Gauss kernel function[J].CAAI Transactions on Intelligent Systems,2015,10(06):436.[doi:10.3969/j.issn.1673-4785.201405049]


更新日期/Last Update: 2018-12-25