Abstract
MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two main functional regulation non-coding RNAs, which involves many important pathological and physiological procedures. Accumulating evidences demonstrated that the interactions between miRNAs and lncRNAs have great impact on modulations of gene expression that are related to many Human diseases. However, identification of miRNA-lncRNA interactions via bio-experimental methods suffers from high cost and time consuming. Thus, it is more and more popular for researchers to utilize computational methods in miRNA-lncRNA interactions prediction because of their high-performance. In this study, we propose a gaussian kernel similarity-based linear optimization model for predicting miRNA-lncRNA interactions. Specifically, gaussian kernel similarity method is employed to learn the miRNAs and lncRNAs similarities based on the observed heterogeneous network. Then, an integrated network is constructed by combining the observed heterogeneous network and the constructed similarities. Finally, a linear optimization model is trained to obtain the rating matrix for the unobserved links in the integrated network. To evaluate the performance of our proposed method, k-fold cross-validation (CV) and leave-one-out cross-validation (LOOCV) are implemented on the collected dataset. The experimental results show that the proposed model yields high AUCs of 0.8624, 0.9053, 0.9152 and 0.9236 in 2-fold, 5-fold, 10-fold CV and LOOCV, respectively. It is anticipated that our proposed method is promising and reliable to inferring the interactions between miRNAs and lncRNAs for further biological researches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bartel, D.P.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004)
Alvarez-Garcia, I., Miska, E.A.: MicroRNA functions in animal development and human disease. Development 132, 4653–4662 (2005)
Baena-Del Valle, J.A., et al.: MYC drives overexpression of telomerase RNA (hTR/TERC) in prostate cancer. J. Pathol. 244, 11–24 (2018)
Gupta, R.A., et al.: Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 464, 1071–1076 (2010)
Clemson, C.M., McNeil, J.A., Willard, H.F., Lawrence, J.B.: XIST RNA paints the inactive X chromosome at interphase: evidence for a novel RNA involved in nuclear/chromosome structure. J. Cell Biol. 132, 259–275 (1996)
Gong, J., Liu, W., Zhang, J., Miao, X., Guo, A.-Y.: lncRNASNP: a database of SNPs in lncRNAs and their potential functions in human and mouse. Nucleic Acids Res. 43, D181–D186 (2015)
Marín, R.M., Vaníček, J.: Efficient use of accessibility in microRNA target prediction. Nucleic Acids Res. 39, 19–29 (2011)
Salmena, L., Poliseno, L., Tay, Y., Kats, L., Pandolfi, P.P.: A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 146, 353–358 (2011)
Yan, Y., Zhang, F., Fan, Q., Li, X., Zhou, K.: Breast cancer-specific TRAIL expression mediated by miRNA response elements of let-7 and miR-122. Neoplasma 61, 672–679 (2014)
Tan, J.Y., et al.: Extensive microRNA-mediated crosstalk between lncRNAs and mRNAs in mouse embryonic stem cells. Genome Res. 25, 655–666 (2015)
Xia, T., et al.: Long noncoding RNA associated-competing endogenous RNAs in gastric cancer. Sci. Rep. 4, 1–7 (2014)
Guo, L.-H., Li, H., Wang, F., Yu, J., He, J.-S.: The tumor suppressor roles of miR-433 and miR-127 in gastric cancer. Int. J. Mol. Sci. 14, 14171–14184 (2013)
Ballantyne, M., McDonald, R., Baker, A.: lncRNA/MicroRNA interactions in the vasculature. Clin. Pharmacol. Ther. 99, 494–501 (2016)
Ma, G., Tang, M., Wu, Y., Xu, X., Pan, F., Xu, R.: LncRNAs and miRNAs: potential biomarkers and therapeutic targets for prostate cancer. Am. J. Transl. Res. 8, 5141 (2016)
Beermann, J., Piccoli, M.-T., Viereck, J., Thum, T.: Non-coding RNAs in development and disease: background, mechanisms, and therapeutic approaches. Physiol. Rev. 96, 1297–1325 (2016)
Kumar, M., Goyal, R.: LncRNA as a therapeutic target for angiogenesis. Curr. Top. Med. Chem. 17, 1750–1757 (2017)
Huang, Y.: The novel regulatory role of lnc RNA-mi RNA-mRNA axis in cardiovascular diseases. J. Cell Mol. Med. 22, 5768–5775 (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)
Coordinators, N.R.: Database resources of the national center for biotechnology information. Nucleic Acids Res. 45, D12 (2017)
Li, J.-H., Liu, S., Zhou, H., Qu, L.-H., Yang, J.-H.: starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 42, D92–D97 (2014)
Lipscomb, C.E.: Medical subject headings (MeSH). Bull. Med. Libr. Assoc. 88, 265 (2000)
Johnson, M., Zaretskaya, I., Raytselis, Y., Merezhuk, Y., McGinnis, S., Madden, T.L.: NCBI BLAST: a better web interface. Nucleic Acids Res. 36, W5–W9 (2008)
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)
You, Z.-H., Lei, Y.-K., Gui, J., Huang, D.-S., Zhou, X.: Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data. Bioinformatics 26, 2744–2751 (2010)
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)
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)
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)
Wang, L., You, Z.-H., Huang, D.-S., Zhou, F.: Combining high speed ELM learning with a deep convolutional neural network feature encoding for predicting protein-RNA interactions. IEEE/ACM Trans. Comput. Biol. Bioinform. 17, 972–980 (2018)
Yi, H.-C., et al.: ACP-DL: a deep learning long short-term memory model to predict anticancer peptides using high-efficiency feature representation. Mol. Ther.-Nucleic Acids 17, 1–9 (2019)
Zheng, K., You, Z.-H., Wang, L., Zhou, Y., Li, L.-P., Li, Z.-W.: MLMDA: a machine learning approach to predict and validate MicroRNA–disease associations by integrating of heterogenous information sources. J. Transl. Med. 17, 260 (2019)
You, Z., Wang, S., Gui, J., Zhang, S.: A novel hybrid method of gene selection and its application on tumor classification. In: Huang, D.-S., Wunsch, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 1055–1068. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85984-0_127
Wang, L., You, Z.-H., Li, L.-P., Zheng, K., Wang, Y.-B.: Predicting circRNA-disease associations using deep generative adversarial network based on multi-source fusion information. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 145–152. IEEE (2019)
Wang, Y., et al.: Predicting protein interactions using a deep learning method-stacked sparse autoencoder combined with a probabilistic classification vector machine. Complexity (2018)
Chen, Z.-H., You, Z.-H., Li, L.-P., Wang, Y.-B., Wong, L., Yi, H.-C.: Prediction of self-interacting proteins from protein sequence information based on random projection model and fast Fourier transform. Int. J. Mol. Sci. 20, 930 (2019)
Li, J., et al.: Using weighted extreme learning machine combined with scale-invariant feature transform to predict protein-protein interactions from protein evolutionary information. IEEE/ACM Trans. Comput. Biol. Bioinform. (2020)
Zhan, Z.-H., You, Z.-H., Zhou, Y., Li, L.-P., Li, Z.-W.: Efficient framework for predicting ncRNA-protein interactions based on sequence information by deep learning. In: Huang, D.-S., Jo, K.-H., Zhang, X.-L. (eds.) ICIC 2018. LNCS, vol. 10955, pp. 337–344. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95933-7_41
Wang, L., You, Z.-H., Li, Y.-M., Zheng, K., Huang, Y.-A.: GCNCDA: a new method for predicting circRNA-disease associations based on graph convolutional network algorithm. PLoS Comput. Biol. 16, e1007568 (2020)
Wang, M.-N., You, Z.-H., Wang, L., Li, L.-P., Zheng, K.: LDGRNMF: LncRNA-disease associations prediction based on graph regularized non-negative matrix factorization. Neurocomputing (2020)
Yue, D., Liu, H., Huang, Y.: Survey of computational algorithms for microRNA target prediction. Curr. Genomics 10, 478–492 (2009)
Guttman, M., Rinn, J.L.: Modular regulatory principles of large non-coding RNAs. Nature 482, 339–346 (2012)
Lewis, B.P., Burge, C.B., Bartel, D.P.: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20 (2005)
Betel, D., Koppal, A., Agius, P., Sander, C., Leslie, C.: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol. 11, R90 (2010)
Wang, X.: Composition of seed sequence is a major determinant of microRNA targeting patterns. Bioinformatics 30, 1377–1383 (2014)
Kertesz, M., Iovino, N., Unnerstall, U., Gaul, U., Segal, E.: The role of site accessibility in microRNA target recognition. Nat. Genet. 39, 1278–1284 (2007)
Rennie, W., et al.: STarMir: a web server for prediction of microRNA binding sites. Nucleic Acids Res. 42, W114–W118 (2014)
Li, J., et al.: LncTar: a tool for predicting the RNA targets of long noncoding RNAs. Brief. Bioinform. 16, 806–812 (2015)
Ab Mutalib, N.-S., Sulaiman, S.A., Jamal, R.: Computational tools for microRNA target prediction. In: Computational Epigenetics and Diseases, pp. 79–105. Elsevier (2019)
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)
Yu, Z., Zhu, F., Tianl, G., Wang, H.: LCBNI: link completion bipartite network inference for predicting new lncRNA-miRNA interactions. In: 2018 IEEE International Conference of Safety Produce Informatization (IICSPI), pp. 873–877. IEEE (2018)
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. Bioinform. (2019)
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. Genomics 11, 17–27 (2018)
Huang, Y.-A., You, Z.-H., Chen, X., Huang, Z.-A., Zhang, S., Yan, G.-Y.: Prediction of microbe–disease association from the integration of neighbor and graph with collaborative recommendation model. J. Transl. Med. 15, 209 (2017)
Pech, R., Hao, D., Lee, Y.-L., Yuan, Y., Zhou, T.: Link prediction via linear optimization. Phys. A: Stat. Mech. Appl. 528, 121319 (2019)
Gong, J., Liu, W., Zhang, J., Miao, X., Guo, A.-Y.: lncRNASNP: a database of SNPs in lncRNAs and their potential functions in human and mouse. Nucleic Acids Res. 43, D181–D186 (2014)
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 (2017)
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)
Acknowledgements
This work is supported by the NSFC Excellent Young Scholars Program, under Grants 61722212, in part by the National Science Foundation of China under Grants 61873212.
Author information
Authors and Affiliations
Contributions
L.W. conceived the project, developed the prediction method, designed the experiments, analyzed the result and wrote the manuscript. Z.H.Y. and Y.A.H analyzed the result and revised the manuscript. X.Z and M.Y.C. analyzed the result. All authors read and approved the manuscript.
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that they have no conflict of interest.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wong, L., You, ZH., Huang, YA., Zhou, X., Cao, MY. (2020). A Gaussian Kernel Similarity-Based Linear Optimization Model for Predicting miRNA-lncRNA Interactions. 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_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-60802-6_28
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)