Advertisement

Predicting MicroRNA-Disease Associations by Random Walking on Multiple Networks

  • Wei PengEmail author
  • Wei Lan
  • Zeng Yu
  • Jianxin Wang
  • Yi Pan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9683)

Abstract

MicroRNA refers to a set of small non-coding RNA which plays important roles in regulating specific mRNA targets and suppressing their expression. Previous researches have verified that the deregulations of microRNA are closely associated with human disease. However it is still a big challenge to design an effective computational method which can integrate multiple biological information to predict microRNA-disease associations. Based on the observation that microRNAs with similar functions tend to associate with common diseases, the diseases sharing similar phenotypes are likely caused by common microRNAs and similar environment factors also affect microRNAs with similar functions and diseases with similar phenotypes. In this work, we design a computational method which can combine microRNA, disease and environmental factors to predict microRNA-disease associations. The method namely ThrRWMDE, takes several steps of random walking on three different biological networks, microRNA-microRNA functional similarity network(MFN), disease-disease similarity network(DSN) and environmental factor similarity network(ESN) respectively so as to get microRNA-disease association information from the neighbors in corresponding networks. In the course of walking, the microRNA-disease association information will also be transferred from one network to another according to the interactions between the nodes in different networks. Our method is not only a framework which can effectively integrate different types of biological methods but also can easily treat these information differently with respect to the topological and structural difference of the three networks. The results of experiment show that our method achieves better prediction performance than other state-of-the-art methods.

Keywords

Biological Network Similarity Network Chemical Structure Similarity Disease Related Gene Good Prediction Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work is supported in part by the National Natural Science Foundation of China under grant no. 61502214, 31560317, 61472133, 61502166, 81460007 and 81560221.

References

  1. 1.
    Jiang, Q., Hao, Y., Wang, G., Juan, L., Zhang, T., Teng, M., Liu, Y., Wang, Y.: Prioritization of disease micrornas through a human phenome-micrornaome network. BMC Syst. Biol. 4(Suppl. 1), S2 (2010)CrossRefGoogle Scholar
  2. 2.
    Xuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., Liu, Y., Dai, Q., Li, J., Teng, Z., et al.: Prediction of micrornas associated with human diseases based on weighted k most similar neighbors. PloS One 8(8), e70204 (2013)CrossRefGoogle Scholar
  3. 3.
    Chen, X., Liu, M.X., Yan, G.Y.: RWRMDA: predicting novel human microrna-disease associations. Mol. BioSyst. 8(10), 2792–2798 (2012)CrossRefGoogle Scholar
  4. 4.
    Chen, X., Yan, G.Y.: Semi-supervised learning for potential human microrna-disease associations inference. Sci. Rep. 4, Article No.5501 (2014)Google Scholar
  5. 5.
    Jiang, Q., Wang, G., Wang, Y.: An approach for prioritizing disease-related micrornas based on genomic data integration. In: 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI), vol. 6, pp. 2270–2274. IEEE (2010)Google Scholar
  6. 6.
    Shi, H., Xu, J., Zhang, G., Xu, L., Li, C., Wang, L., Zhao, Z., Jiang, W., Guo, Z., Li, X.: Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes. BMC Syst. Biol. 7(1), 101 (2013)CrossRefGoogle Scholar
  7. 7.
    Chen, H., Zhang, Z.: Similarity-based methods for potential human microRNA-disease association prediction. BMC Med. Genom. 6(1), 12 (2013)CrossRefGoogle Scholar
  8. 8.
    Lan, W., Wang, J., Li, M., Liu, J., Pan, Y.: Predicting microRNA-disease associations by integrating multiple biological information. In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 183–188. IEEE (2015)Google Scholar
  9. 9.
    Zeng, X., Zhang, X., Zou, Q.: Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Brief. Bioinform. 17, 193–203 (2015)CrossRefGoogle Scholar
  10. 10.
    Das, U.N.: Obesity: genes, brain, gut, and environment. Nutrition 26(5), 459–473 (2010)CrossRefGoogle Scholar
  11. 11.
    Yang, Q., Qiu, C., Yang, J., Wu, Q., Cui, Q.: miREnvironment database: providing a bridge for microRNAs, environmental factors and phenotypes. Bioinformatics 27(23), 3329–3330 (2011)CrossRefGoogle Scholar
  12. 12.
    Qiu, C., Chen, G., Cui, Q.: Towards the understanding of microRNA and environmental factor interactions and their relationships to human diseases. Sci. Rep. 2, Article No.318 (2012)Google Scholar
  13. 13.
    Chen, X., Liu, M.X., Cui, Q.H., Yan, G.Y.: Prediction of disease-related interactions between microRNAs and environmental factors based on a semi-supervised classifier. PloS One 7(8), e43425 (2012)CrossRefGoogle Scholar
  14. 14.
    Li, J., Wu, Z., Cheng, F., Li, W., Liu, G., Tang, Y.: Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology. Sci. Rep. 4, Article No.5576 (2014)Google Scholar
  15. 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(13), 1644–1650 (2010)CrossRefGoogle Scholar
  16. 16.
    Li, Y., Qiu, C., Tu, J., Geng, B., Yang, J., Jiang, T., Cui, Q.: Hmdd v2. 0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Res. gkt1023, 1–5 (2013)Google Scholar
  17. 17.
    Cheng, L., Li, J., Ju, P., Peng, J., Wang, Y.: SemFunSim: a new method for measuring disease similarity by integrating semantic and gene functional association. PloS One 9(6), e99415 (2014)CrossRefGoogle Scholar
  18. 18.
    Hattori, M., Okuno, Y., Goto, S., Kanehisa, M.: Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc. 125(39), 11853–11865 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Wei Peng
    • 1
    • 2
    Email author
  • Wei Lan
    • 3
  • Zeng Yu
    • 2
  • Jianxin Wang
    • 3
  • Yi Pan
    • 2
  1. 1.Computer CenterKunming University of Science and TechnologyKunmingChina
  2. 2.Department of Computer ScienceGeorgia State UniversityAtlantaUSA
  3. 3.School of Information Science and EngineeringCentral South UniversityChangshaChina

Personalised recommendations