A Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA)

Ahmet Toprak

doi:10.29137/umagd.1217754

EN TR

A Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA)

Abstract

miRNAs, a subclass of non-coding small RNAs, are about 18-22 nucleotides long. It has been revealed that miRNAs are responsible many diseases such as cancer. Therefore, great efforts have been made recently by researchers to explore possible relationships between miRNAs and diseases. Experimental studies to identify new disease-associated miRNAs are very expensive and at the same time a long process. Therefore, to determine the relationships between miRNA and disease many computational methods have been developed. In this paper, a new method for the identification of miRNA-disease associations based on space projection and label propagation (SPLPMDA) is proposed. The forecast the precision of SPLPMDA was demonstrated using 5-fold cross-validation and LOOCV techniques. Values of 0.9333 in 5-fold cross validation and 0.9441 in LOOCV were obtained. Moreover, case studies on breast neoplasms and lymphoma were performed to further confirm the predictive reliability of SPLPMDA.

Keywords

miRNA, disease, miRNA-disease association, space projection, label propagation

References

Alexiou, P., Vergoulis, T., Gleditzsch, M., Prekas, G., Dalamagas, T., Megraw, M., . . . Hatzigeorgiou, A. G. (2009). miRGen 2.0: a database of microRNA genomic information and regulation. Nucleic Acids Research, 38(suppl_1), D137-D141. doi:10.1093/nar/gkp888
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., . . . Yu, X. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. nature, 403(6769), 503-511.
Bartel, D. P. (2009). MicroRNAs: target recognition and regulatory functions. cell, 136(2), 215-233. doi:10.1016/j.cell.2009.01.002
Cai, J., Liu, X., Cheng, J., Li, Y., Huang, X., Li, Y., . . . Wei, R. (2012). MicroRNA-200 is commonly repressed in conjunctival MALT lymphoma, and targets cyclin E2. Graefe's Archive for Clinical and Experimental Ophthalmology, 250(4), 523-531.
Chandra, S., Vimal, D., Sharma, D., Rai, V., Gupta, S. C., & Chowdhuri, D. K. (2017). Role of miRNAs in development and disease: Lessons learnt from small organisms. Life Sci, 185, 8-14. doi:10.1016/j.lfs.2017.07.017
Chen, Q., Lai, D., Lan, W., Wu, X., Chen, B., Liu, J., . . . Wang, J. (2021). ILDMSF: Inferring Associations Between Long Non-Coding RNA and Disease Based on Multi-Similarity Fusion. IEEE/ACM Trans Comput Biol Bioinform, 18(3), 1106-1112. doi:10.1109/TCBB.2019.2936476
Chen, X., Wang, L. Y., & Huang, L. (2018). NDAMDA: Network distance analysis for MiRNA-disease association prediction. Journal of Cellular and Molecular Medicine, 22(5), 2884-2895. doi:10.1111/jcmm.13583
Chen, X., Xie, D., Wang, L., Zhao, Q., You, Z. H., & Liu, H. (2018). BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction. Bioinformatics, 34(18), 3178-3186. doi:10.1093/bioinformatics/bty333
Chen, X., Xie, D., Zhao, Q., & You, Z.-H. (2019). MicroRNAs and complex diseases: from experimental results to computational models. Briefings in Bioinformatics, 20(2), 515-539. doi:10.1093/bib/bbx130
Chen, X., Yan, C. C., Zhang, X., You, Z. H., Deng, L., Liu, Y., . . . Dai, Q. (2016). WBSMDA: Within and Between Score for MiRNA-Disease Association prediction. Scientific reports, 6, 21106. doi:10.1038/srep21106

Chen, X., Yin, J., Qu, J., & Huang, L. (2018). MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction. PLoS Comput Biol, 14(8), e1006418. doi:10.1371/journal.pcbi.1006418
Chen, X., Zhou, Z., & Zhao, Y. (2018). ELLPMDA: ensemble learning and link prediction for miRNA-disease association prediction. RNA biology, 15(6), 807-818. doi:10.1080/15476286.2018.1460016
DeSantis, C. E., Fedewa, S. A., Goding Sauer, A., Kramer, J. L., Smith, R. A., & Jemal, A. (2016). Breast cancer statistics, 2015: Convergence of incidence rates between black and white women. CA: a cancer journal for clinicians, 66(1), 31-42.
DeSantis, C. E., Ma, J., Goding Sauer, A., Newman, L. A., & Jemal, A. (2017). Breast cancer statistics, 2017, racial disparity in mortality by state. CA: a cancer journal for clinicians, 67(6), 439-448.
Espinosa, C. E. S., & Slack, F. J. (2006). Cancer issue: the role of microRNAs in cancer. The Yale journal of biology and medicine, 79(3-4), 131-140.
Feber, A., Xi, L., Luketich, J. D., Pennathur, A., Landreneau, R. J., Wu, M., . . . Litle, V. R. (2008). MicroRNA expression profiles of esophageal cancer. The Journal of thoracic and cardiovascular surgery, 135(2), 255-260.
Gao, Y., Jia, K., Shi, J., Zhou, Y., & Cui, Q. (2019). A Computational Model to Predict the Causal miRNAs for Diseases. Front Genet, 10, 935. doi:10.3389/fgene.2019.00935
Iorio, M. V., Ferracin, M., Liu, C.-G., Veronese, A., Spizzo, R., Sabbioni, S., . . . Campiglio, M. (2005). MicroRNA gene expression deregulation in human breast cancer. Cancer research, 65(16), 7065-7070.
Jiang, Q., Wang, Y., Hao, Y., Juan, L., Teng, M., Zhang, X., . . . Liu, Y. (2009). miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res, 37(Database issue), D98-104. doi:10.1093/nar/gkn714
Kim, Y.-K. (2015). Extracellular microRNAs as biomarkers in human disease. Chonnam medical journal, 51(2), 51-57. doi:10.4068/cmj.2015.51.2.51
Kozomara, A., & Griffiths-Jones, S. (2013). miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Research, 42(D1), D68-D73. doi:10.1093/nar/gkt1181
Lan, W., Wang, J., Li, M., Liu, J., Wu, F. X., & Pan, Y. (2018). Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(6), 1774-1782. doi:10.1109/TCBB.2016.2586190
Lee, R. C., Feinbaum, R. L., & Ambros, V. (1993). The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. cell, 75(5), 843-854.
Li, J.-Q., Rong, Z.-H., Chen, X., Yan, G.-Y., & You, Z.-H. (2017). MCMDA: Matrix completion for MiRNA-disease association prediction. Oncotarget, 8(13), 21187. doi:10.18632/oncotarget.15061
Li, Y., Qiu, C., Tu, J., Geng, B., Yang, J., Jiang, T., & Cui, Q. (2014). HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Research, 42(Database issue), D1070-D1074. doi:10.1093/nar/gkt1023
McGirt, L. Y., Adams, C. M., Baerenwald, D. A., Zwerner, J. P., Zic, J. A., & Eischen, C. M. (2014). miR-223 regulates cell growth and targets proto-oncogenes in mycosis fungoides/cutaneous T-cell lymphoma. Journal of Investigative Dermatology, 134(4), 1101-1107.
Osada, H., & Takahashi, T. (2011). let‐7 and miR‐17‐92: Small‐sized major players in lung cancer development. Cancer science, 102(1), 9-17.
Pech, R., Lee, Y.-L., Hao, D., Po, M., & Zhou, T. (2019). LOMDA: Linear optimization for miRNA-disease association prediction. doi:10.1101/751651
Qu, J., Zhao, Y., & Yin, J. (2019). Identification and Analysis of Human Microbe-Disease Associations by Matrix Decomposition and Label Propagation. Front Microbiol, 10, 291. doi:10.3389/fmicb.2019.00291
Saydam, F., Değirmenci, İ., & Güneş, H. V. (2011). MikroRNA'lar ve kanser. Dicle Tıp Dergisi, 38(1). Selcuklu, S. D., Donoghue, M. T., & Spillane, C. (2009). miR-21 as a key regulator of oncogenic processes. Biochem Soc Trans, 37(Pt 4), 918-925. doi:10.1042/BST0370918
Tan, W., Liu, B., Qu, S., Liang, G., Luo, W., & Gong, C. (2018). MicroRNAs and cancer: Key paradigms in molecular therapy. Oncol Lett, 15(3), 2735-2742. doi:10.3892/ol.2017.7638
Tang, C., Zhou, H., Zheng, X., Zhang, Y., & Sha, X. (2019). Dual Laplacian regularized matrix completion for microRNA-disease associations prediction. RNA biology, 16(5), 601-611. doi:10.1080/15476286.2019.1570811
Toprak, A., & Eryilmaz Dogan, E. (2021). Prediction of Potential MicroRNA-Disease Association Using Kernelized Bayesian Matrix Factorization. Interdiscip Sci, 13(4), 595-602. doi:10.1007/s12539-021-00469-w
Toprak, A., & Eryilmaz, E. (2021). Prediction of miRNA-disease associations based on Weighted K-Nearest known neighbors and network consistency projection. J Bioinform Comput Biol, 19(1), 2050041. doi:10.1142/S0219720020500419
van Laarhoven, T., Nabuurs, S. B., & Marchiori, E. (2011). Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 27(21), 3036-3043. doi:10.1093/bioinformatics/btr500
Vural, H., & Kaya, M. (2018). Prediction of new potential associations between LncRNAs and environmental factors based on KATZ measure. Computers in biology and medicine, 102, 120-125. doi:10.1016/j.compbiomed.2018.09.019
Wang, C. C., Chen, X., Yin, J., & Qu, J. (2019). An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy. RNA biology, 16(3), 257-269. doi:10.1080/15476286.2019.1568820
Wang, D., Wang, J., Lu, M., Song, F., & Cui, Q. (2010). Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics, 26(13), 1644-1650. doi:10.1093/bioinformatics/btq241
Watanabe, A., Tagawa, H., Yamashita, J., Teshima, K., Nara, M., Iwamoto, K., . . . Nakagawa, T. (2011). The role of microRNA-150 as a tumor suppressor in malignant lymphoma. Leukemia, 25(8), 1324-1334.
Yan, F., Zheng, Y., Jia, W., Hou, S., & Xiao, R. (2019). MAMDA: Inferring microRNA-Disease associations with manifold alignment. Comput Biol Med, 110, 156-163. doi:10.1016/j.compbiomed.2019.05.014
Yang, J.-H., Shao, P., Zhou, H., Chen, Y.-Q., & Qu, L.-H. (2009). deepBase: a database for deeply annotating and mining deep sequencing data. Nucleic Acids Research, 38(suppl_1), D123-D130. doi:10.1093/nar/gkp943
Yang, Z., Ren, F., Liu, C., He, S., Sun, G., Gao, Q., . . . Zhao, H. (2010). dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genomics, 11 Suppl 4, S5. doi:10.1186/1471-2164-11-S4-S5
Yin, M. M., Liu, J. X., Gao, Y. L., Kong, X. Z., & Zheng, C. H. (2022). NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation. IEEE Trans Cybern, 52(6), 5079-5087. doi:10.1109/TCYB.2020.3026652
Yu, S. P., Liang, C., Xiao, Q., Li, G. H., Ding, P. J., & Luo, J. W. (2019). MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation. J Cell Mol Med, 23(2), 1427-1438. doi:10.1111/jcmm.14048

Details

Primary Language

English

Subjects

Engineering, Electrical Engineering

Journal Section

Research Article

Authors

Ahmet Toprak ^*
0000-0003-3337-4917
Türkiye

Publication Date

December 31, 2022

Submission Date

December 12, 2022

Acceptance Date

December 23, 2022

Published in Issue

Year 2022 Volume: 14 Number: 3

DOI

https://doi.org/10.29137/umagd.1217754

IZ

https://izlik.org/JA52PP52DC

APA

Toprak, A. (2022). A Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA). International Journal of Engineering Research and Development, 14(3), 234-243. https://doi.org/10.29137/umagd.1217754

AMA

1.Toprak A. A Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA). IJERAD. 2022;14(3):234-243. doi:10.29137/umagd.1217754

Chicago

Toprak, Ahmet. 2022. “A Novel Method for MiRNA-Disease Association Prediction Based on Space Projection and Label Propagation (SPLPMDA)”. International Journal of Engineering Research and Development 14 (3): 234-43. https://doi.org/10.29137/umagd.1217754.

EndNote

Toprak A (December 1, 2022) A Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA). International Journal of Engineering Research and Development 14 3 234–243.

IEEE

[1]A. Toprak, “A Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA)”, IJERAD, vol. 14, no. 3, pp. 234–243, Dec. 2022, doi: 10.29137/umagd.1217754.

ISNAD

Toprak, Ahmet. “A Novel Method for MiRNA-Disease Association Prediction Based on Space Projection and Label Propagation (SPLPMDA)”. International Journal of Engineering Research and Development 14/3 (December 1, 2022): 234-243. https://doi.org/10.29137/umagd.1217754.

JAMA

1.Toprak A. A Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA). IJERAD. 2022;14:234–243.

MLA

Toprak, Ahmet. “A Novel Method for MiRNA-Disease Association Prediction Based on Space Projection and Label Propagation (SPLPMDA)”. International Journal of Engineering Research and Development, vol. 14, no. 3, Dec. 2022, pp. 234-43, doi:10.29137/umagd.1217754.

Vancouver

1.Ahmet Toprak. A Novel Method for miRNA-Disease Association Prediction based on Space Projection and Label Propagation (SPLPMDA). IJERAD. 2022 Dec. 1;14(3):234-43. doi:10.29137/umagd.1217754

Cited By

circRNA-disease association prediction with an improved unbalanced Bi-Random walk

Journal of Radiation Research and Applied Sciences

https://doi.org/10.1016/j.jrras.2024.100858

Kırıkkale University, Faculty of Engineering and Natural Science, 71450 Yahşihan / Kırıkkale, Türkiye.

ijerad@kku.edu.tr