Skip to main content
SpringerLink
Log in

Application of Matrix Tri-Factorization for Predicting miRNA-Disease Associations

  • Conference paper
  • First Online:
Advanced Computing and Intelligent Technologies

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 914))

  • 875 Accesses

Abstract

Recent research on miRNAs has shown that miRNAs play an important role in disease progression, leading to the investigation of the discovery of miRNA-disease affiliation. For predicting miRNA-disease affiliation, matrix tri-factorization is used. The functional similarity of miRNA, the similarity of miRNA based solely on diseases, the similarity of miRNA based solely on environmental factors, and adjacency matrix for miRNA and disease had been calculated and considered in the proposed approach. We also took the semantic similarity of the diseases into account. We tested the proposed method on eight diseases; the method is robust, and the results of the experiments ranked 50 miRNAs for gastric cancers, 48 miRNAs for breast cancers, and 48 miRNAs for kidney cancers with in the top 50 predictions. We obtained the area under the curve of 0.90967.

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 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover 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. Ines, A.G., Miska, E.A.: Microrna functions in animal development and human disease. Development 132(21), 4653–4662 (2005)

    Article  Google Scholar 

  2. Ines, A.G., Miska, E.A.: Micrornas: genomics, biogenesis, mechanism, and function. Cell 116(21), 281–297 (2004)

    Google Scholar 

  3. Lee, R.C., Feinbaum, R.L., Ambros, V.: The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 75(5):843–854 (1993)

    Google Scholar 

  4. Karp, X.: Encountering micrornas in cell fate signaling. Science 310(5752), 1288–1289 (2005)

    Article  Google Scholar 

  5. Cheng, A.M.: Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Res. 33(5752), 1290–1297 (2005)

    Article  Google Scholar 

  6. Miska, E.A.: How micrornas control cell division, differentiation and death. Curr Opin Genet Dev. 15(5), 563–568 (2005)

    Article  Google Scholar 

  7. Jopling, C.L., Minkyung, Y., Lancaster, A.M., Lemon, S.M., Peter, S.: Modulation of hepatitis C virus RNA abundance by a liver-specific microRNA. Science 309(5740), 1577–1581 (2005)

    Article  Google Scholar 

  8. Chen, X., Liu, M., Yan, G.: RWRMDA: predicting novel human microRNA—disease associations. Mol. BioSyst. 2792–2798 (2012). https://doi.org/10.1039/c2mb25180a

  9. Chen, X., Yan, C.C., Zhang, X., You, Z.-H., Huang, Y.-A., Yan, G.-Y.: HGIMDA: heterogeneous graph inference for miRNA disease association prediction. Oncotarget 7(40), 65257–65269 (2016)

    Google Scholar 

  10. Jiang, Y., Liu, B., Yu, L., Yan, C., Bian, H.: Predict miRNA-disease association with collaborative filtering. Neuroinformatics 16(3–4), 363–372 (2018)

    Article  Google Scholar 

  11. You, Z.-H., Huang, Z.-A., Zhu, Z., Yan, G.-Y., Li, Z.-W., Wen, Z., Chen, X.: Pbmda: a novel and effective path-based computational model for miRNA-disease association prediction. PLoS Comput. Biol. 13(3), 1005455 (2017)

    Article  Google Scholar 

  12. Chen, X., Yan, G.Y.: Semi-supervised learning for potential human microRNA-disease associations inference. Sci Rep. 30(4), 5501 (2014). https://doi.org/10.1038/srep05501.PMID:24975600;PMCID:PMC4074792

    Article  Google Scholar 

  13. Li, J.Q., Rong, Z.H., Chen, X., Yan, G.Y., You, Z.H.: MCMDA: matrix completion for MiRNA-disease association prediction. Oncotarget 28;8(13), 21187–21199 (2017). https://doi.org/10.18632/oncotarget.15061. PMID: 28177900; PMCID: PMC5400576

  14. Gu, C., Liao, B., Li, X., et al.: Network consistency projection for Human miRNA-disease associations inference. Sci Rep 6, 36054 (2016). https://doi.org/10.1038/srep36054

    Article  Google Scholar 

  15. Shen, Z., Zhang, Y.-H., Han, K., Nandi, A.K., Honig, B., Huang, D.S.: miRNA-disease association prediction with collaborative matrix factorization. Complexity 2017, 9 (2017). Article ID 2498957. https://doi.org/10.1155/2017/2498957

  16. Gao, Y.-L., Cui, Z., Liu, J.-X., Wang, J., Zheng, C.-H.: NPCMF: nearest profile-based collaborative matrix factorization method for predicting miRNA-disease associations. BMC Bioinform. 20(1), 353 (2019)

    Article  Google Scholar 

  17. Mridha, K., et al.: Plant disease detection using web application by neural network. In: 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), pp. 130–136 (2021). https://doi.org/10.1109/ICCCA52192.2021.9666354

  18. Huang, F., Qiu, Y., Li, Q., Liu, S., Ni, F.: Predicting drug-disease associations via multi-task learning based on collective matrix factorization. Front. Bioeng. Biotechnol. 8, 218 (2020). https://doi.org/10.3389/fbioe.2020.00218

    Article  Google Scholar 

  19. Cai, D., He, X., Han, J., Huang, T.S.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1548–1560 (2011). https://doi.org/10.1109/TPAMI.2010.231

    Article  Google Scholar 

  20. 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).https://doi.org/10.1093/bioinformatics/btq241

  21. Ha, J., Kim, H., Yoon, Y., Park, S.: A method of extracting disease-related microRNAs through the propagation algorithm using the environmental factor based global miRNA network. Bio-Med. Mater. Eng. 26, S1763–S1772 (2015). https://doi.org/10.3233/BME-151477

  22. Yang, Q., Qiu, C., Yang, J., Wu, Q., Cui, Q.: miREnvironment database: providing a bridge for microRNAs, environmental factors and phenotypes. Bioinformatics 27, 3329–3330 (2011)

    Article  Google Scholar 

  23. 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(Database issue), D1070–D1074 (2014). https://doi.org/10.1093/nar/gkt1023

  24. Chen, X., You, Z., Yan, G., Gong, D.: IRWRLDA: improved random walk with restart for lncRNA-disease association prediction. Oncotarget 7, 57919±57931 (2016). PMID: 2751731. https://doi.org/10.18632/oncotarget.11141

  25. Huang, Y., Chen, X., You, Z., Huang, D., Chan, K.: ILNCSIM: improved lncRNA functional similarity calculation model. Oncotarget 7, 25902±25914 (2016). PMID. https://doi.org/10.18632/oncotarget.8296

  26. Palimkar, P., et al.: Machine learning technique to prognosis diabetes disease: random forest classifier approach. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds.) Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, vol. 218. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-2164-2_19

  27. Belkin, M., Partha, N., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399–2434 (2006). Available online at: http://www.jmlr.org/papers/v7/belkin06a.html

  28. Ma, Y., Fu, Y.: Manifold Learning Theory and Applications. CRC; Taylor & Francis distributor, Boca Raton, FL (2012). https://doi.org/10.1201/b11431

    Book  Google Scholar 

  29. Zhang, W., Liu, X., Chen, Y., Wu, W., Wang, W., Li, X.: Feature derived graph regularized matrix factorization for predicting drug side effects. Neurocomputing 287, 154–162 (2018). https://doi.org/10.1016/j.neucom.2018.01.085

    Article  Google Scholar 

  30. Chung, F.R.K.: Spectral graph theory. Providence, R.I.: Published for the Conference Board of the mathematical sciences by the American Mathematical Society. (1997)

    Google Scholar 

  31. Rana, B., Juneja, A., Saxena, M., Gudwani, S., Kumaran, S.S., Behari, M., et al.: Graph-theory-based spectral feature selection for computer aided diagnosis of Parkinson’s disease using T1-weighted MRI. Int. J. Imag. Syst. Technol. 25, 245–255 (2015). https://doi.org/10.1002/ima.22141

    Article  Google Scholar 

  32. Zhang, W., Chen, Y., Tu, S., Liu, F., Qu, Q.: Drug side effect prediction through linear neighborhoods and multiple data source integration. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (Shenzhen: BIBM), pp. 427–434 (2016a). https://doi.org/10.1109/BIBM.2016.7822555

  33. Zhang, W., Chen, Y., Li, D.: Drug-target interaction prediction through label propagation with linear neighborhood information. Molecules 22, 2056 (2017). https://doi.org/10.3390/molecules22122056

    Article  Google Scholar 

  34. Mukhopadhyay, M., et al.: Facial emotion recognition based on textural pattern and convolutional neural network. In: 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), pp. 1–6 (2021). https://doi.org/10.1109/GUCON50781.2021.9573860

  35. Zhang, W., Yue, X., Liu, F., Chen, Y.L., Tu, S.K., Zhang, X.N.: A unified frame of predicting side effects of drugs by using linear neighborhood similarity. BMC Syst. Biol. 11(Suppl. 6), 101 (2017). https://doi.org/10.1186/s12918-017-0477-2

    Article  Google Scholar 

  36. Zhang, W., Qu, Q., Zhang, Y., Wang, W.: The linear neighborhood propagation method for predicting long non-coding RNA–protein interactions. Neurocomputing 273, 526–534 (2018). https://doi.org/10.1016/j.neucom.2017.07.065

    Article  Google Scholar 

  37. Yang, Z., Ren, F., Liu, C., He, S., Sun, G., Gao, Q., et al.: DBDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genomics 11(Suppl. 4), S5 (2010). 10.1186%2F1471-2164-11-S4-S5

    Google Scholar 

  38. Xie, B., Ding, Q., Han, H., Di, W.: miRCancer: a microRNA–cancer association database constructed by text mining on literature. Bioinformatics 29(5), 638–644 (2013). https://doi.org/10.1093/bioinformatics/btt014

    Article  Google Scholar 

  39. Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1–122 (2011). https://doi.org/10.1561/2200000016

    Article  MATH  Google Scholar 

  40. Yu, H.-F., Jain, P., Kar, P., Dhillon, I.S.: Large-scale multi-label learning with missing labels. In: Proceedings of the 31st International Conference on International Conference on Machine Learning. (Beijing: JMLR.org) (2014)

    Google Scholar 

Download references

Declaration of Competing Interest

The authors declare that they have no competing interests.

Author information

Authors and Affiliations

  1. Department of Studies in Computer Science, University of Mysore, Mysore, India

    J. R. Rashmi & Lalitha Rangarajan

Authors
  1. J. R. Rashmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lalitha Rangarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. R. Rashmi .

Editor information

Editors and Affiliations

  1. Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

    Rabindra Nath Shaw

  2. Regional Campus Manipur, Indira Gandhi National Tribal University, Imphal, Manipur, India

    Sanjoy Das

  3. Department of Computer Science, University of Milan, Milan, Italy

    Vincenzo Piuri

  4. Department of Information Engineering and Mathematics, University of Siena, Siena, Italy

    Monica Bianchini

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rashmi, J.R., Rangarajan, L. (2022). Application of Matrix Tri-Factorization for Predicting miRNA-Disease Associations. In: Shaw, R.N., Das, S., Piuri, V., Bianchini, M. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Electrical Engineering, vol 914. Springer, Singapore. https://doi.org/10.1007/978-981-19-2980-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-2980-9_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-2979-3

  • Online ISBN: 978-981-19-2980-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation