Abstract
Accumulating evidence demonstrated that microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) are related with some complex human diseases. LncRNA-miRNA interactions (LMIs) play an important role in regulatory of gene networks. However, the biological experiments for detecting lncRNA-miRNA interactions are often expensive and time-consuming. Thus, it is urgent to develop computational method for predicting LMIs. In this paper, we propose a novel computational approach LMMAN to predict potential lncRNA-miRNA associations based on molecular associations network (MAN). More specifically, the known relationships among miRNA, lncRNA, protein, drug and disease are firstly integrated to construct a molecular association network. Then, a network embedding model LINE is employed to extract network behavior features of lncRNA and miRNA nodes. Finally, the random forest classifier is used to predict the potential lncRNA-miRNA interactions. In order to evaluate the performance of the proposed LMMAN approach, five-fold cross-validation tests are implemented on benchmark dataset lncRNASNP2. The proposed LMMAN approach can achieve the high AUC of 0.9644, which is obviously better than the existing methods. The promising results reveal that LMMAN can effectively predict new lncRNA-miRNA interactions and can be a good complement to relevant biomedical fields in the future.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhou, S., Xuan, Z., Wang, L., Ping, P., Pei, T.: A novel model for predicting associations between diseases and LncRNA-miRNA pairs based on a newly constructed bipartite network. Comput. Math. Methods Med. 2018, 6789089 (2018)
Holley, C.L., Topkara, V.K.: An introduction to small non-coding RNAs: miRNA and snoRNA. Cardiovasc. Drugs Ther. 25, 151–159 (2011)
Lin, S.L., Miller, J.D., Ying, S.Y.: Intronic microRNA (miRNA). J. Biomed. Biotechnol. 2006, 26818 (2006)
Chen, X., Gong, Y., Zhang, D.H., You, Z.H., Li, Z.W.: DRMDA: deep representations-based miRNA–disease association prediction. J. Cell Mol. Med. 22, 472–485 (2018)
Chen, X., et al.: A novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction. Mol. BioSyst. 13, 1202–1212 (2017)
Chen, X., Xie, D., Wang, L., Zhao, Q., You, Z.-H., Liu, H.: BNPMDA: bipartite network projection for MiRNA–disease association prediction. Bioinformatics 34, 3178–3186 (2018)
Chen, X., et al.: WBSMDA: within and between score for MiRNA-disease association prediction. Sci. Rep. 6, 21106 (2016)
Chen, X., Yan, C.C., Zhang, X., You, Z.-H., Huang, Y.-A., Yan, G.-Y.: HGIMDA: heterogeneous graph inference for miRNA-disease association prediction. Oncotarget 7, 65257 (2016)
Chen, X., Zhang, D.-H., You, Z.-H.: A heterogeneous label propagation approach to explore the potential associations between miRNA and disease. J. Transl. Med. 16, 348 (2018)
Huang, Y.-A., Hu, P., Chan, K.C., You, Z.-H.: Graph convolution for predicting associations between miRNA and drug resistance. Bioinformatics 36, 851–858 (2020)
Bouba, I., Kang, Q., Luan, Y.S., Meng, J.: Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants. Math. Biosci. 312, 67–76 (2019)
Li, J.-Q., Rong, Z.-H., Chen, X., Yan, G.-Y., You, Z.-H.: MCMDA: matrix completion for MiRNA-disease association prediction. Oncotarget 8, 21187 (2017)
Qu, J., et al.: In Silico prediction of small molecule-miRNA associations based on the HeteSim algorithm. Mol. Ther.-Nucleic Acids 14, 274–286 (2019)
Sun, Y., Zhu, Z., You, Z.-H., Zeng, Z., Huang, Z.-A., Huang, Y.-A.: FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases. BMC Syst. Biol. 12, 121 (2018)
Wang, L., et al.: LMTRDA: using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities. PLoS Comput. Biol. 15, e1006865 (2019)
You, Z.-H., et al.: PBMDA: a novel and effective path-based computational model for miRNA-disease association prediction. PLoS Comput. Biol. 13, e1005455 (2017)
You, Z.-H., et al.: PRMDA: personalized recommendation-based MiRNA-disease association prediction. Oncotarget 8, 85568 (2017)
Zheng, K., You, Z.-H., Wang, L., Zhou, Y., Li, L.-P., Li, Z.-W.: Dbmda: a unified embedding for sequence-based mirna similarity measure with applications to predict and validate mirna-disease associations. Mol. Ther.-Nucleic Acids 19, 602–611 (2020)
Chen, X., Yan, C.C., Zhang, X., You, Z.-H.: Long non-coding RNAs and complex diseases: from experimental results to computational models. Brief. Bioinf. 18, 558–576 (2017)
Chen, X., You, Z.-H., Yan, G.-Y., Gong, D.-W.: IRWRLDA: improved random walk with restart for lncRNA-disease association prediction. Oncotarget 7, 57919 (2016)
Guo, Z.-H., You, Z.-H., Wang, Y.-B., Yi, H.-C., Chen, Z.-H.: A learning-based method for lncRNA-disease association identification combing similarity information and rotation forest. iScience 19, 786–795 (2019)
Yi, H.-C., et al.: Learning distributed representations of RNA and protein sequences and its application for predicting lncRNA-protein interactions. Comput. Struct. Biotechnol. J. 18, 20–26 (2020)
Huang, Y.-A., Chan, K.C., You, Z.-H.: Constructing prediction models from expression profiles for large scale lncRNA–miRNA interaction profiling. Bioinformatics 34, 812–819 (2018)
Quinn, J.J., Chang, H.Y.: Unique features of long non-coding RNA biogenesis and function. Nat. Rev. Genet. 17, 47–62 (2016)
Jeggari, A., Marks, D.S., Larsson, E.: miRcode: a map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics 28, 2062–2063 (2012)
Chen, X., Huang, Y.-A., Wang, X.-S., You, Z.-H., Chan, K.C.: FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model. Oncotarget 7, 45948 (2016)
Hu, P., Huang, Y.-A., Chan, K.C., You, Z.-H.: Learning multimodal networks from heterogeneous data for prediction of lncRNA-miRNA interactions. IEEE/ACM Trans. Comput. Biol. Bioinf. (2019)
Huang, Y.-A., Chen, X., You, Z.-H., Huang, D.-S., Chan, K.C.: ILNCSIM: improved lncRNA functional similarity calculation model. Oncotarget 7, 25902 (2016)
Huang, Z.A., Huang, Y.A., You, Z.H., Zhu, Z., Sun, Y.: Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph. BMC Med. Genom. 11, 113 (2018)
Huang, Z.-A., et al.: Predicting lncRNA-miRNA interaction via graph convolution auto-encoder. Front. Genet. 10, 758 (2019)
Wang, M.-N., You, Z.-H., Li, L.-P., Wong, L., Chen, Z.-H., Gan, C.-Z.: GNMFLMI: graph regularized nonnegative matrix factorization for predicting LncRNA-MiRNA interactions. IEEE Access 8, 37578–37588 (2020)
Wong, L., Huang, Y.A., You, Z.H., Chen, Z.H., Cao, M.Y.: LNRLMI: linear neighbour representation for predicting lncRNA-miRNA interactions. J. Cell. Mol. Med. 24, 79–87 (2020)
Jalali, S., Bhartiya, D., Lalwani, M.K., Sivasubbu, S., Scaria, V.: Systematic transcriptome wide analysis of lncRNA-miRNA interactions. PLoS One 8, e53823 (2013)
Yoon, J.-H., Abdelmohsen, K., Gorospe, M.: Functional interactions among microRNAs and long noncoding RNAs. Seminar. Cell Dev. Biol. 34, 9–14 (2014)
Veneziano, D., Marceca, G.P., Di Bella, S., Nigita, G., Distefano, R., Croce, C.M.: Investigating miRNA-lncRNA interactions: computational tools and resources. Methods Mol. Biol. 1970, 251–277 (2019)
Hu, P., Huang, Y.-A., Chan, K.C.C., You, Z.-H.: Discovering an integrated network in heterogeneous data for predicting lncRNA-miRNA Interactions. In: Huang, D.-S., Bevilacqua, V., Premaratne, P., Gupta, P. (eds.) ICIC 2018. LNCS, vol. 10954, pp. 539–545. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95930-6_51
Chen, X.: Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA. Sci. Rep. 5, 13186 (2015)
Zhang, W., Tang, G., Wang, S., Chen, Y., Zhou, S., Li, X.: Sequence-derived linear neighborhood propagation method for predicting lncRNA-miRNA interactions. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 50–55. IEEE (2018)
Zhang, W., Qu, Q., Zhang, Y., Wang, W.: The linear neighborhood propagation method for predicting long non-coding RNA–protein interactions. Neurocomputing 273, 526–534 (2018)
Guo, Z.-H., Yi, H.-C., You, Z.-H.: Construction and comprehensive analysis of a molecular association network via lncRNA–miRNA–disease–drug–protein graph. Cells 8, 866 (2019)
Miao, Y.R., Liu, W., Zhang, Q., Guo, A.Y.: lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs. Nucleic Acids Res. 46, D276–D280 (2018)
Huang, Z., et al.: HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 47, D1013–D1017 (2019)
Chou, C.H., et al.: miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 46, D296–D302 (2018)
Chen, G., et al.: LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Res. 41, D983–D986 (2012)
Jiang, Q., et al.: LncRNA2Target: a database for differentially expressed genes after lncRNA knockdown or overexpression. Nucleic Acids Res. 43, D193–D196 (2014)
Piñero, J., et al.: DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. gkw943 (2016)
Wishart, D.S., et al.: DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082 (2017)
Davis, A.P., et al.: The comparative toxicogenomics database: update 2019. Nucleic Acids Res. 47, D948–D954 (2018)
Szklarczyk, D., et al.: The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. gkw937 (2016)
Kozomara, A., Birgaoanu, M., Griffiths-Jones, S.: miRBase: from microRNA sequences to function. Nucleic Acids Res. 47, D155–D162 (2018)
Fang, S., et al.: NONCODEV5: a comprehensive annotation database for long non-coding RNAs. Nucleic Acids Res. 46, D308–D314 (2017)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067-1077. International World Wide Web Conferences Steering Committee (2015)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701-710. ACM (2014)
Kirk, J.M., et al.: Functional classification of long non-coding RNAs by k-mer content. Nat. Genet. 50, 1474 (2018)
Amaratunga, D., Cabrera, J., Lee, Y.-S.: Enriched random forests. Bioinformatics 24, 2010–2014 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, BW., Zhang, P., You, ZH., Zhou, JR., Li, X. (2020). Predicting LncRNA-miRNA Interactions via Network Embedding with Integrated Structure and Attribute Information. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-60802-6_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60801-9
Online ISBN: 978-3-030-60802-6
eBook Packages: Computer ScienceComputer Science (R0)