Abstract
As the function of lncRNA is gradually understood, they have been found regulating the expression of target genes at the post-transcriptional level, and their abnormal functions may lead to so many diseases. Then, identifying the lncRNA-disease associations (LDA) can help to better understand its pathogenesis, promote the search for biomarkers of disease diagnosis, and effectively prevent disease. To break through the limitations of the existing computational models, we put forward a novel computational method of lncRNA-disease association identification by employing Fast Kernel Learning with Kronecker Regularized Least Squares (FKL-KronRLS-LDA). This model first extracts three different similarity kernels in disease and lncRNA space respectively. Next, it fuses these distinct kernels into an integrated kernel with the optimized combining weightings indicating their importance. It then combines lncRNA kernel and disease kernel into one larger kernel by Kronecker product kernel. Finally, it adopts the regularization least squares to identify potential associations. In experiments of Leave one out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), FKL-KronRLS-LDA respectively obtains an AUC of 0.917 and 0.856, which outperform other excellent computational models. Furthermore, in the case studies, 9, 8 and 8 out of top 10 identified lncRNAs are successfully confirmed by recent published literature for lung cancer, breast cancer and gastric cancer, respectively. In a word, FKL-KronRLS-LDA can effectively identify potential lncRNA-disease associations for human beings.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Crick, F.H.C., Barnett, L., Brenner, S., Watts-Tobin, R.J.: General nature of the genetic code for proteins. Nature 192(4809), 1227–1232 (1961)
Bian, E., Li, J., Xie, Y., Zong, G., Li, J., Zhao, B.: LncRNAs: new players in gliomas, with special emphasis on the interaction of lncRNAs With EZH2. J. Cell. Physiol. 230(3), 496–503 (2015)
Liang, C., Li, Y., Luo, J.W.: A novel method to detect functional microRNA regulatory modules by bicliques merging. IEEE/ACM Trans. Comput. Biol. Bioinform. (2016)
Xiao, Q., et al.: Identifying lncRNA and mRNA co-expression modules from matched expression data in ovarian cancer. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Institute of Electrical and Electronics Engineers Inc., August 2018
Liu, Y., Luo, J., Ding, P.: Inferring MicroRNA targets based on restricted Boltzmann machines. IEEE J. Biomed. Heal., Informatics (2019)
Luo, J., Ding, P., Liang, C., Cao, B., Chen, X.: Collective prediction of disease-associated miRNAs based on transduction learning. IEEE/ACM Trans. Comput. Biol. Bioinform. 14, 1468–1475 (2017)
Luo, J., Long, Y.: NTSHMDA: prediction of human microbe-disease association based on random walk by integrating network topological similarity. IEEE/ACM Trans. Comput. Biol. Bioinform. 17, 1341–1351 (2018)
Sun, J., et al.: Inferring novel lncRNA-disease associations based on a random walk model of a lncRNA functional similarity network. Mol. BioSyst. 10(8), 2074–2081 (2014)
Chen, X.: KATZLDA: KATZ measure for the lncRNA-disease association prediction. Sci. Rep. 5, 1–11 (2015)
Zitnik, M., Nguyen, F., Wang, B., Leskovec, J., Goldenberg, A., Hoffman, M.M.: Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. Inf. Fusion 50(June 2018), 71–91 (2019)
Chen, X., Yan, G.Y.: Novel human lncRNA-disease association inference based on lncRNA expression profiles. Bioinformatics 29(20), 2617–2624 (2013)
Lan, W., et al.: LDAP : A Web Server for lncRNA-Disease Association Prediction, vol. 33, no. October 2016, pp. 458–460 (2017)
Li, W., Wang, S., Xu, J., Mao, G., Tian, G., Yang, J.: Inferring latent disease-lncRNA associations by faster matrix completion on a heterogeneous network. Front. Genet. (2019)
Fu, G., Wang, J., Domeniconi, C., Yu, G.: Matrix factorization-based data fusion for the prediction of lncRNA-disease associations. Bioinformatics 34(9), 1529–1537 (2018)
Lu, C., et al.: Prediction of lncRNA-disease associations based on inductive matrix completion. Bioinformatics 34(19), 3357–3364 (2018)
Peng, L., Liao, B., Zhu, W., Li, Z., Li, K.: Predicting drug-target interactions with multi-information fusion. IEEE J. Biomed. Heal. Inf. (2017)
Zeng, X., et al.: Network-based prediction of drug–target interactions using an arbitrary-order proximity embedded deep forest. Bioinformatics 36, 1–8 (2020)
Ding, Y., Tang, Y., Guo, F.: Identification of protein-protein interactions via a novel matrix-based sequence representation model with amino acid contact information. Int. J. Mol. Sci. (2016)
Chen, X., Niu, Y.W., Wang, G.H., Yan, G.Y.: MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction. J. Transl. Med. 15(1), 1–14 (2017)
Xie, G., Meng, T., Luo, Y., Liu, Z.: SKF-LDA: similarity kernel fusion for predicting lncRNA-disease association. Mol. Ther. Nucl. Acids 18(December), 45–55 (2019)
Kuang, Q., et al.: A kernel matrix dimension reduction method for predicting drug-target interaction. Chemom. Intell. Lab. Syst. 162(November 2015), 104–110 (2017)
Shen, C., Ding, Y., Tang, J., Jiang, L., Guo, F.: LPI-KTASLP: prediction of LncRNA-protein interaction by semi-supervised link learning with multivariate information. IEEE Access 7, 13486–13496 (2019)
Hu, J., Li, Y., Zhang, M., Yang, X., Shen, H., Yu, D.: Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs, vol. 14, no. 6, pp. 1389–1398 (2017)
He, J., Chang, S.F., Xie, L.: Fast kernel learning for spatial pyramid matching. In: 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)
Ding, H., Takigawa, I., Mamitsuka, H., Zhu, S.: Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Brief. Bioinform. (2013)
Bao, Z., Yang, Z., Huang, Z., Zhou, Y., Cui, Q., Dong, D.: LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases. Nucl. Acids Res. 47(D1), D1034–D1037 (2019)
Wang, D., Wang, J., Lu, M., Song, F., Cui, Q.: Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 26(13), 1644–1650 (2010)
Package, T., Disease, T., Semantic, O., Annotationdbi, I.: Package. In: DOSE (2019)
Piñero, J., et al.: DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucl. Acids Res. 45(D1), D833–D839 (2017)
Durinck, S., et al.: BioMart and bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005)
Aken, B.L., et al.: The ensemble gene annotation system. In: Database (Oxford) (2016)
Mott, R.: Smith-Waterman algorithm. In: Encyclopedia of Life Sciences (2005
Yu, G., Wang, L.G., Yan, G.R., He, Q.Y.: DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics 31, 608–609 (2015)
van Laarhoven, T., Nabuurs, S.B., Marchiori, E.: Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27, 3036–3043 (2011)
Shen, C., Ding, Y., Tang, J., Guo, F.: Multivariate information fusion with fast kernel learning to Kernel Ridge Regression in predicting lncRNA-protein interactions. Front. Genet. 10(Jan), 1–12 (2019)
Jiang, L., Xiao, L., Ding, Y., Tang, J., Guo, F.: FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association. BMC Genom. 19(Suppl 10) (2018)
Kuang, Q., et al.: A kernel matrix dimension reduction method for predicting drug-target interaction. Chemom. Intell. Lab. Syst. 162(November 2016), 104–110 (2017)
Cui, T., et al.: MNDR v2.0: an updated resource of ncRNA-disease associations in mammals. Nucl. Acids Res. (2018)
Gao, Y., et al.: Lnc2Cancer v2.0: updated database of experimentally supported long non-coding RNAs in human cancers. Nucleic Acids Res. 47(D1), D1028–D1033 (2019)
Jia, K., Gao, Y., Shi, J., Zhou, Y., Zhou, Y., Cui, Q.: Annotation and curation of the causality information in LncRNADisease. In: Database (Oxford) (2020)
Pan, G.F., Zhou, X.F., Zhao, J.P.: Correlation between expression of long non-coding RNA ZXF1 and prognosis of lung adenocarcinoma and its potential molecular mechanism. Zhonghua Zhong Liu Za Zhi 39(2), 102–108 (2017)
Pan, Y., et al.: The emerging roles of long noncoding RNA ROR (lincRNA-ROR) and its possible mechanisms in human cancers. In: Cellular Physiology and Biochemistry, vol. 40, no. 1–2, pp. 219–229. S. Karger AG, November 2016
Fan, S., et al.: Downregulation of the long non-coding RNA TUG1 is associated with cell proliferation, migration, and invasion in breast cancer. Biomed. Pharmacother. 95, 1636–1643 (2017)
Guo, X., et al.: GAS5 inhibits gastric cancer cell proliferation partly by modulating CDK6. Oncol. Res. Treat. 38(7–8), 362–366 (2015)
Acknowledgement
This work is supported by the National Nature Science Foundation of China (Grant Nos. 61472467 and 61672011), and the National Key Research and Development Program (Grant Nos. 2017YFC1311003).
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
Li, W., Wang, SL., Xu, J., Yang, J. (2020). Identification of Human LncRNA-Disease Association by Fast Kernel Learning-Based Kronecker Regularized Least Squares. 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_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-60802-6_27
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)