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Identifying lncRNA-Disease Relationships via Heterogeneous Clustering

  • Emanuele Pio Barracchia
  • Gianvito PioEmail author
  • Donato Malerba
  • Michelangelo Ceci
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10785)

Abstract

High-throughput sequencing technology led significant advances in functional genomics, giving the opportunity to pay particular attention to the role of specific biological entities. Recently, researchers focused on long non-coding RNAs (lncRNAs), i.e. transcripts that are longer than 200 nucleotides which are not transcribed into proteins. The main motivation comes from their influence on the development of human diseases. However, known relationships between lncRNAs and diseases are still poor and their in-lab validation is still expensive. In this paper, we propose a computational approach, based on heterogeneous clustering, which is able to predict possibly unknown lncRNA-disease relationships by analyzing complex heterogeneous networks consisting of several interacting biological entities of different types. The proposed method exploits overlapping and hierarchically organized heterogeneous clusters, which are able to catch multiple roles of lncRNAs and diseases at different levels of granularity. Our experimental evaluation, performed on a heterogeneous network consisting of microRNAs, lncRNAs, diseases, genes and their known relationships, shows that the proposed method is able to obtain better results with respect to existing methods.

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Notes

Acknowledgements

We would like to acknowledge the support of the European Commission through the projects MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data (Grant Number ICT-2013-612944) and TOREADOR - Trustworthy Model-aware Analytics Data Platform (Grant Number H2020-688797).

References

  1. 1.
    Alaimo, S., Giugno, R., Pulvirenti, A.: ncPred: ncRNA-disease association prediction through tripartite network-based inference. Front. Bioeng. Biotechnol. 2, 71 (2014)CrossRefGoogle Scholar
  2. 2.
    Bauer-Mehren, A., Rautschka, M., Sanz, F., Furlong, L.I.: DisGeNET: a cytoscape plugin to visualize, integrate, search and analyze gene-disease networks. Bioinformatics 26(22), 2924–2926 (2010)CrossRefGoogle Scholar
  3. 3.
    Cech, T., Steitz, J.: The noncoding RNA revolution-trashing old rules to forge new ones. Cell 157(1), 77–94 (2014)CrossRefGoogle Scholar
  4. 4.
    Ceci, M., Pio, G., Kuzmanovski, V., Dzeroski, S.: Semi-supervised multi-view learning for gene network reconstruction. PLOS ONE 10(12), 1–27 (2015)CrossRefGoogle Scholar
  5. 5.
    Chen, G., Wang, Z., Wang, D., Qiu, C., Liu, M., Chen, X., Zhang, Q., Yan, G., Cui, Q.: LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Rese. 41(D1), D983–D986 (2013)CrossRefGoogle Scholar
  6. 6.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)zbMATHGoogle Scholar
  7. 7.
    Hayes, J., Peruzzi, P.P., Lawler, S.: MicroRNAs in cancer: biomarkers, functions and therapy. Trends Mol. Med. 20(8), 460–469 (2014)CrossRefGoogle Scholar
  8. 8.
    Helwak, A., Kudla, G., Dudnakova, T., Tollervey, D.: Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 153(3), 654–665 (2013)CrossRefGoogle Scholar
  9. 9.
    Jiang, Q., Wang, Y., Hao, Y., Juan, L., Teng, M., Zhang, X., Li, M., Wang, G., Liu, Y.: miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 37(suppl 1), D98–D104 (2009)CrossRefGoogle Scholar
  10. 10.
    Lesmo, L., Saitta, L., Torasso, P.: Evidence combination in expert systems. Int. J. Man-Mach. Stud. 22(3), 307–326 (1985)CrossRefGoogle Scholar
  11. 11.
    Melissari, M.T., Grote, P.: Roles for long non-coding RNAs in physiology and disease. Pflügers Archiv - Eur. J. Physiol. 468(6), 945–958 (2016)CrossRefGoogle Scholar
  12. 12.
    Pio, G., Ceci, M., D’Elia, D., Loglisci, C., Malerba, D.: A novel biclustering algorithm for the discovery of meaningful biological correlations between microRNAs and their target genes. BMC Bioinformatics 14(S-7), S8 (2013)Google Scholar
  13. 13.
    Pio, G., Ceci, M., Malerba, D., D’Elia, D.: ComiRNet: a web-based system for the analysis of miRNA-gene regulatory networks. BMC Bioinformatics 16(9), S7 (2015)Google Scholar
  14. 14.
    Pio, G., Malerba, D., D’Elia, D., Ceci, M.: Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach. BMC Bioinformatics 15(1), S4 (2014)Google Scholar
  15. 15.
    Pio, G., Serafino, F., Malerba, D., Ceci, M.: Multi-type clustering and classification from heterogeneous networks. Inf. Sci. 425, 107–126 (2018)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Yang, X., Gao, L., Guo, X., Shi, X., Wu, H., Song, F., et al.: A network based method for analysis of lncRNA-disease associations and prediction of lncRNAs implicated in diseases. PLOS ONE (2014)Google Scholar
  17. 17.
    Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefzbMATHGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Bari Aldo MoroBariItaly
  2. 2.CINI - Consorzio Interuniversitario Nazionale per l’InformaticaBariItaly

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