Skip to main content
SpringerLink
Log in

A Novel Computational Method for MiRNA-Disease Association Prediction

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

Included in the following conference series:

  • 3023 Accesses

Abstract

Accumulating biological and clinical reports have indicated that disorders of microRNAs (miRNAs) are closely related with occurrence and development of various complex human diseases. Developing computational models to infer potential miRNA-disease associations has attracted increasing attention. In this paper, we developed the model of Improved Random Walk with Restart for MiRNA-Disease Association prediction (IRWRMDA) to identify potentially related miRNAs for investigated diseases. By taking advantages of known miRNA-disease association network and miRNA functional similarity network, IRWRMDA obtained reliable performance with AUC of 0.8208. What’s more, Colon Neoplasms and Kidney Neoplasms were taken as case studies, where 45 and 43 out of the top 50 predicted miRNAs were successfully confirmed by recent clinical researches, respactively. It is anticipated that IRWRMDA would serve as an important biological resource for future experimental guidance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., et al.: Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001)

    Article  Google Scholar 

  2. Claverie, J.M.: Fewer genes, more noncoding RNA. Science 309, 1529–1530 (2005)

    Article  Google Scholar 

  3. Birney, E., Stamatoyannopoulos, J.A., Dutta, A., Guigo, R., Gingeras, T.R., Margulies, E.H., et al.: Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007)

    Article  Google Scholar 

  4. Gutschner, T., Diederichs, S.: The hallmarks of cancer: a long non-coding RNA point of view. RNA Biol. 9, 703–719 (2012)

    Article  Google Scholar 

  5. Eddy, S.R.: Non-coding RNA genes and the modern RNA world. Nat. Rev. Genet. 2, 919–929 (2001)

    Article  Google Scholar 

  6. Bartel, D.P.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004)

    Article  Google Scholar 

  7. Ambros, V.: The functions of animal microRNAs. Nature 431, 350–355 (2004)

    Article  Google Scholar 

  8. Jopling, C.L., Yi, M., Lancaster, A.M., Lemon, S.M., Sarnow, P.: Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science 309, 1577–1581 (2005)

    Article  Google Scholar 

  9. Vasudevan, S., Tong, Y., Steitz, J.A.: Switching from repression to activation: microRNAs can up-regulate translation. Science 318, 1931–1934 (2007)

    Article  Google Scholar 

  10. Griffiths-Jones, S., Saini, H.K., van Dongen, S., Enright, A.J.: miRBase: tools for microRNA genomics. Nucleic Acids Res. 36, D154–D158 (2008)

    Article  Google Scholar 

  11. Xu, J., Li, C.X., Lv, J.Y., Li, Y.S., Xiao, Y., Shao, T.T., et al.: Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: case study of prostate cancer. Mol. Cancer Ther. 10, 1857–1866 (2011)

    Article  Google Scholar 

  12. Chen, X., Yan, G.Y.: Semi-supervised learning for potential human microRNA-disease associations inference. Sci. Rep. 4, 5501 (2014)

    Article  Google Scholar 

  13. Li, Y., Qiu, C., Tu, J., Geng, B., Yang, J., Jiang, T., et al.: HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. 42, D1070–D1074 (2014)

    Article  Google Scholar 

  14. Chen, X., Yan, C.C., Zhang, X., Li, Z., Deng, L., Zhang, Y., et al.: RBMMMDA: predicting multiple types of disease-microRNA associations. Sci. Rep. 5, 13877 (2015)

    Article  Google Scholar 

  15. 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, 1644–1650 (2010)

    Article  Google Scholar 

  16. Gu, C., Liao, B., Li, X., Li, K.: Network consistency projection for human miRNA-disease associations inference. Sci. Rep. 6, 36054 (2016)

    Article  Google Scholar 

  17. Chen, X., Liu, M.X., Yan, G.Y.: RWRMDA: predicting novel human microRNA-disease associations. Mol. BioSyst. 8, 2792–2798 (2012)

    Article  Google Scholar 

  18. Stewart, B., Wild, C.P.: World cancer report 2014. World (2016)

    Google Scholar 

  19. Jemal, A., Bray, F., Center, M.M., Ferlay, J., Ward, E., Forman, D.: Global cancer statistics. CA Cancer J. Clin. 61, 69–90 (2011)

    Article  Google Scholar 

  20. Shi, B., Sepp-Lorenzino, L., Prisco, M., Linsley, P., de Angelis, T., Baserga, R.: Micro RNA 145 targets the insulin receptor substrate-1 and inhibits the growth of colon cancer cells. J. Biol. Chem. 282, 32582–32590 (2007)

    Article  Google Scholar 

  21. Diaz, R., Silva, J., Garcia, J.M., Lorenzo, Y., Garcia, V., Pena, C., et al.: Deregulated expression of miR-106a predicts survival in human colon cancer patients. Genes Chromosomes Cancer 47, 794–802 (2008)

    Article  Google Scholar 

  22. Slaby, O., Svoboda, M., Fabian, P., Smerdova, T., Knoflickova, D., Bednarikova, M., et al.: Altered expression of miR-21, miR-31, miR-143 and miR-145 is related to clinicopathologic features of colorectal cancer. Oncology 72, 397–402 (2007)

    Article  Google Scholar 

  23. Chai, H., Liu, M., Tian, R., Li, X., Tang, H.: miR-20a targets BNIP2 and contributes chemotherapeutic resistance in colorectal adenocarcinoma SW480 and SW620 cell lines. Acta Biochim. Biophys. Sin. (Shanghai) 43, 217–225 (2011)

    Article  Google Scholar 

  24. Earle, J.S., Luthra, R., Romans, A., Abraham, R., Ensor, J., Yao, H., et al.: Association of microRNA expression with microsatellite instability status in colorectal adenocarcinoma. J. Mol. Diagn. 12, 433–440 (2010)

    Article  Google Scholar 

  25. Zhang, G.J., Xiao, H.X., Tian, H.P., Liu, Z.L., Xia, S.S., Zhou, T.: Upregulation of microRNA-155 promotes the migration and invasion of colorectal cancer cells through the regulation of claudin-1 expression. Int. J. Mol. Med. 31, 1375–1380 (2013)

    Google Scholar 

  26. Nishida, N., Yokobori, T., Mimori, K., Sudo, T., Tanaka, F., Shibata, K., et al.: MicroRNA miR-125b is a prognostic marker in human colorectal cancer. Int. J. Oncol. 38, 1437–1443 (2011)

    Article  Google Scholar 

  27. Deng, S.-P., Zhu, L., Huang, D.S.: Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks. BMC Genom. 16(Suppl 3), S4 (2015)

    Article  Google Scholar 

  28. Zheng, C.-H., Zhang, L., Ng, V.T.-Y., Shiu, S.C.-K., Huang, D.S.: Molecular pattern discovery based on penalized matrix decomposition. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(6), 1592–1603 (2011)

    Article  Google Scholar 

  29. Senanayake, U., Das, S., Vesely, P., Alzoughbi, W., Frohlich, L.F., Chowdhury, P., et al.: miR-192, miR-194, miR-215, miR-200c and miR-141 are downregulated and their common target ACVR2B is strongly expressed in renal childhood neoplasms. Carcinogenesis 33, 1014–1021 (2012)

    Article  Google Scholar 

  30. Zaman, M.S., Shahryari, V., Deng, G., Thamminana, S., Saini, S., Majid, S., et al.: Up-regulation of microRNA-21 correlates with lower kidney cancer survival. PLoS ONE 7, e31060 (2012)

    Article  Google Scholar 

  31. von Brandenstein, M., Pandarakalam, J.J., Kroon, L., Loeser, H., Herden, J., Braun, G., et al.: MicroRNA 15a, inversely correlated to PKCalpha, is a potential marker to differentiate between benign and malignant renal tumors in biopsy and urine samples. Am. J. Pathol. 180, 1787–1797 (2012)

    Article  Google Scholar 

  32. Xiong, M., Jiang, L., Zhou, Y., Qiu, W., Fang, L., Tan, R., et al.: The miR-200 family regulates TGF-beta1-induced renal tubular epithelial to mesenchymal transition through Smad pathway by targeting ZEB1 and ZEB2 expression. Am. J. Physiol. Renal Physiol. 302, F369–F379 (2012)

    Article  Google Scholar 

  33. Huang, D.S., Zhang, L., Han, K., Deng, S., Yang, K., Zhang, H.: Prediction of protein-protein interactions based on protein-protein correlation using least squares regression. Curr. Protein Pept. Sci. 15(6), 553–560 (2014)

    Article  Google Scholar 

  34. Wang, G., Kwan, B.C., Lai, F.M., Chow, K.M., Li, P.K., Szeto, C.C.: Elevated levels of miR-146a and miR-155 in kidney biopsy and urine from patients with IgA nephropathy. Dis. Markers 30, 171–179 (2011)

    Article  Google Scholar 

  35. Huang, D.S., Yu, H.-J.: Normalized feature vectors: a novel alignment-free sequence comparison method based on the numbers of adjacent amino acids. IEEE/ACM Trans. Comput. Biol. Bioinform. 10(2), 457–467 (2013)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by the grants of the National Science Foundation of China, Nos. 61472282, 61520106006, 31571364, U1611265, 61672203, 61402334, 61472280, 6 1532008, 61472173, 61572447, 61373098 and 61672382, China Postdoctoral Science Foundation Grant, Nos. 2016M601646.

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Zhi-Chao Jiang, Zhen Shen & Wenzheng Bao

Authors
  1. Zhi-Chao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenzheng Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhi-Chao Jiang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Jiang, ZC., Shen, Z., Bao, W. (2017). A Novel Computational Method for MiRNA-Disease Association Prediction. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63309-1_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation