Skip to main content
SpringerLink
Log in
Book cover

International Conference on Computational Advances in Bio and Medical Sciences

ICCABS 2019: Computational Advances in Bio and Medical Sciences pp 102–120Cite as

A Multi-hypothesis Learning Algorithm for Human and Mouse miRNA Target Prediction

  • Conference paper
  • First Online:

  • 2354 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 12029))

Abstract

MicroRNAs (miRNAs) are small non-coding RNAs that play a key role in regulating gene expression and thus in many cellular activities. Dysfunction of cells in these tasks is correlated with the development of several kinds of cancer. As the functionality of miRNAs depends on the location of their binding on their targets, binding site prediction has received a lot of attention in the last several years. Despite its importance, the mechanisms of miRNA targeting are still unknown. In this paper, we introduce an algorithm that partitions miRNA target duplexes according to hypotheses that each represents a different mechanism of targeting. The algorithm, called multi-hypothesis learner, examines all possible hypotheses to find out the optimum data partitions according to the performance of these hypotheses for miRNA target prediction. These hypotheses were then utilized to build a superior target predictor for miRNAs. Our method exploited biologically meaningful features for recognizing targets, which enables establishment of hypotheses that can be correlated with target recognition mechanisms. Test results show that the algorithm can provide comparable performance to state-of-the-art machine learning tools such as RandomForest in predicting miRNA binding sites. Moreover, feature selection on the partitions in our method confirms that the partitioning mechanism is closely related to biological mechanisms of miRNA targeting. The resulting data partitions can potentially be used for in vivo experiments to aid in discovery of the targeting mechanisms.

This work was supported in part by NIH grant (award No: R01GM117596), as a part of Joint DMS/NIGMS Initiative to Support Research at the Interface of the Biological and Mathematical Sciences, and NSF IIS grant (award No: 0916250).

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Agarwal, V., Bell, G.W., Nam, J.W., Bartel, D.P.: Predicting effective microrna target sites in mammalian mRNAs. Elife 4, e05005 (2015)

    Article  Google Scholar 

  2. Bartel, D.P.: MicroRNAs: target recognition and regulatory functions. Cell 136(2), 215–233 (2009)

    Article  Google Scholar 

  3. Betel, D., Koppal, A., Agius, P., Sander, C., Leslie, C.: Comprehensive modeling of microrna targets predicts functional non-conserved and non-canonical sites. Genome Biol. 11(8), R90 (2010)

    Article  Google Scholar 

  4. Betel, D., Wilson, M., Gabow, A., Marks, D.S., Sander, C.: The microRNA. org resource: targets and expression. Nucleic Acids Res. 36(suppl 1), D149–D153 (2008)

    Google Scholar 

  5. Brennecke, J., Stark, A., Russell, R.B., Cohen, S.M.: Principles of microrna-target recognition. PLoS Biol. 3(3), e85 (2005)

    Article  Google Scholar 

  6. Cloonan, N.: Re-thinking miRNA-mRNA interactions: intertwining issues confound target discovery. BioEssays 37(4), 379–388 (2015)

    Article  Google Scholar 

  7. Ding, J., Li, X., Hu, H.: TarPmiR: a new approach for microrna target site prediction. Bioinformatics 32(18), 2768–2775 (2016)

    Article  Google Scholar 

  8. Dweep, H., Sticht, C., Pandey, P., Gretz, N.: miRWalk-database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J. Biomed. Inf. 44(5), 839–847 (2011)

    Article  Google Scholar 

  9. Enright, A.J., et al.: MicroRNA targets in drosophila. Genome Biol. 5(1), R1–R1 (2004)

    Article  Google Scholar 

  10. Friedman, R.C., Farh, K.K.H., Burge, C.B., Bartel, D.P.: Most mammalian mRNAs are conserved targets of micrornas. Genome Res. 19(1), 92–105 (2009)

    Article  Google Scholar 

  11. Genome.gov. https://www.genome.gov/10001345/. Accessed 06 Jan 2017

  12. Ghoshal, A., Grama, A., Bagchi, S., Chaterji, S.: An ensemble SVM model for the accurate prediction of non-canonical microRNA targets. In: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 403–412. ACM (2015)

    Google Scholar 

  13. Griffiths-Jones, S., Grocock, R.J., Van Dongen, S., Bateman, A., Enright, A.J.: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34(suppl–1), D140–D144 (2006)

    Article  Google Scholar 

  14. Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato (1999)

    Google Scholar 

  15. Hsu, S.D., et al.: miRtarBase update 2014: an information resource for experimentally validated mirna-target interactions. Nucleic Acids Res. 42(D1), D78–D85 (2014)

    Article  Google Scholar 

  16. Jansson, M.D., Lund, A.H.: MicroRNA and cancer. Mol. Oncol. 6(6), 590–610 (2012)

    Article  Google Scholar 

  17. John, B., Enright, A.J., Aravin, A., Tuschl, T., Sander, C., Marks, D.S.: Human microrna targets. PLoS Biol. 2(11), e363 (2004)

    Article  Google Scholar 

  18. Kim, S.K., Nam, J.W., Rhee, J.K., Lee, W.J., Zhang, B.T.: miTarget: microRNA target gene prediction using a support vector machine. BMC Bioinf. 7(1), 411 (2006)

    Article  Google Scholar 

  19. Kiriakidou, M., et al.: A combined computational-experimental approach predicts human microrna targets. Genes Dev. 18(10), 1165–1178 (2004)

    Article  Google Scholar 

  20. Lewis, B.P., Burge, C.B., Bartel, D.P.: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120(1), 15–20 (2005)

    Article  Google Scholar 

  21. Lewis, B.P., Shih, I.H., Jones-Rhoades, M.W., Bartel, D.P., Burge, C.B.: Prediction of mammalian microRNA targets. Cell 115(7), 787–798 (2003)

    Article  Google Scholar 

  22. Lin, S., Gregory, R.I.: MicroRNA biogenesis pathways in cancer. Nat. Rev. Cancer 15(6), 321–333 (2015)

    Article  Google Scholar 

  23. Loeb, G.B., et al.: Transcriptome-wide mir-155 binding map reveals widespread noncanonical microrna targeting. Mol. Cell 48(5), 760–770 (2012)

    Article  Google Scholar 

  24. Lorenz, R., et al.: Viennarna package 2.0. Algorithms Mol. Biol. 6(1), 26 (2011)

    Article  Google Scholar 

  25. Witkos, T.M., Koscianska, E., Krzyzosiak, W.J.: Practical aspects of microRNA target prediction. Curr. Mol. Med. 11(2), 93–109 (2011)

    Article  Google Scholar 

  26. Maragkakis, M., et al.: DIANA-microT web server: elucidating microRNA functions through target prediction. Nucleic Acids Res. 37(suppl–2), W273–W276 (2009)

    Article  Google Scholar 

  27. Maziere, P., Enright, A.J.: Prediction of microrna targets. Drug Discov. Today 12(11–12), 452–458 (2007)

    Article  Google Scholar 

  28. Mohebbi, M., Ding, L., Malmberg, R.L., Momany, C., Rasheed, K., Cai, L.: Accurate prediction of human miRNA targets via graph modeling of miRNA-target duplex. J. Bioinf. Comput. Biol. (2018). https://doi.org/10.1142/S0219720018500130

  29. Peterson, S.M., Thompson, J.A., Ufkin, M.L., Sathyanarayana, P., Liaw, L., Congdon, C.B.: Common features of microrna target prediction tools. Front Genet 5, 23 (2014)

    Article  Google Scholar 

  30. Rehmsmeier, M., Steffen, P., Höchsmann, M., Giegerich, R.: Fast and effective prediction of microRNA/target duplexes, vol. 10. Cold Spring Harbor Lab (2004)

    Google Scholar 

  31. Rusinov, V., Baev, V., Minkov, I.N., Tabler, M.: Microinspector: a web tool for detection of miRNA binding sites in an RNA sequence. Nucleic Acids Res. 33(suppl 2), W696–W700 (2005)

    Article  Google Scholar 

  32. Schirle, N.T., Sheu-Gruttadauria, J., MacRae, I.J.: Structural basis for microrna targeting. Science 346(6209), 608–613 (2014)

    Article  Google Scholar 

  33. Sturm, M., Hackenberg, M., Langenberger, D., Frishman, D.: TargetSpy: a supervised machine learning approach for microrna target prediction. BMC Bioinf. 11(1), 1 (2010)

    Article  Google Scholar 

  34. Sujit Pal, A.G.: Deep Learning with Keras. Packt Publishing, Birmingham (2017)

    Google Scholar 

  35. Thompson, S.K.: Sampling. Wiley, Hoboken (2012)

    Book  MATH  Google Scholar 

  36. Wang, X.: miRDB: a microRNA target prediction and functional annotation database with a wiki interface. RNA 14(6), 1012–1017 (2008)

    Article  Google Scholar 

  37. Weill, N., Lisi, V., Scott, N., Dallaire, P., Pelloux, J., Major, F.: Mirbooking simulates the stoichiometric mode of action of micrornas. Nucleic Acids Res. 43(14), 6730–6738 (2015)

    Article  Google Scholar 

  38. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)

    Google Scholar 

  39. Yue, D., Liu, H., Huang, Y.: Survey of computational algorithms for microRNA target prediction. Curr. Genom. 10(7), 478–492 (2009)

    Article  Google Scholar 

Download references

Acknowledgement

The authors would like to thank Dr. Cory Momany and Dr. Khaled Rasheed for their feedbacks and comments on this work.

Author information

Authors and Affiliations

  1. Department of Computer Science, Appalachian State University, Boone, NC, 28608, USA

    Mohammad Mohebbi

  2. St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA

    Liang Ding

  3. Department of Plant Biology, The University of Georgia, Athens, GA, 30602, USA

    Russell L. Malmberg

  4. Department of Computer Science, The University of Georgia, Athens, GA, 30602, USA

    Liming Cai

Authors
  1. Mohammad Mohebbi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Russell L. Malmberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liming Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Mohebbi .

Editor information

Editors and Affiliations

  1. University of Connecticut, Storrs, CT, USA

    Ion Măndoiu

  2. Virginia Tech, Blacksburg, VA, USA

    T. M. Murali

  3. Florida International University, Miami, FL, USA

    Giri Narasimhan

  4. University of Connecticut, Storrs, CT, USA

    Sanguthevar Rajasekaran

  5. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  6. Georgia State University, Atlanta, GA, USA

    Alexander Zelikovsky

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mohebbi, M., Ding, L., Malmberg, R.L., Cai, L. (2020). A Multi-hypothesis Learning Algorithm for Human and Mouse miRNA Target Prediction. In: Măndoiu, I., Murali, T., Narasimhan, G., Rajasekaran, S., Skums, P., Zelikovsky, A. (eds) Computational Advances in Bio and Medical Sciences. ICCABS 2019. Lecture Notes in Computer Science(), vol 12029. Springer, Cham. https://doi.org/10.1007/978-3-030-46165-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-46165-2_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46164-5

  • Online ISBN: 978-3-030-46165-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation