LOCANDA: Exploiting Causality in the Reconstruction of Gene Regulatory Networks

  • Gianvito PioEmail author
  • Michelangelo Ceci
  • Francesca Prisciandaro
  • Donato Malerba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10558)


The reconstruction of gene regulatory networks via link prediction methods is receiving increasing attention due to the large availability of data, mainly produced by high throughput technologies. However, the reconstructed networks often suffer from a high amount of false positive links, which are actually the result of indirect regulation activities. Such false links are mainly due to the presence of common cause and common effect phenomena, which are typically present in gene regulatory networks. Existing methods for the identification of a transitive reduction of a network or for the removal of (possibly) redundant links suffer from limitations about the structure of the network or the nature/length of the indirect regulation, and often require additional pre-processing steps to handle specific peculiarities of the networks at hand (e.g., cycles).

In this paper, we propose the method LOCANDA, which overcomes these limitations and is able to identify and exploit indirect relationships of arbitrary length to remove links considered as false positives. This is performed by identifying indirect paths in the network and by comparing their reliability with that of direct links. Experiments performed on networks of two organisms (E. coli and S. cerevisiae) show a higher accuracy in the reconstruction with respect to the considered competitors, as well as a higher robustness to the presence of noise in the data.


Causality Bionformatics Gene network reconstruction 



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).


  1. 1.
    Aho, A.V., Garey, M.R., Ullman, J.D.: The transitive reduction of a directed graph. SIAM J. Comput. 1(2), 131–137 (1972)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Atias, N., Sharan, R.: Comparative analysis of protein networks: hard problems, practical solutions. Commun. ACM 55(5), 88–97 (2012)CrossRefGoogle Scholar
  3. 3.
    Bošnački, D., Odenbrett, M.R., Wijs, A., Ligtenberg, W., Hilbers, P.: Efficient reconstruction of biological networks via transitive reduction on general purpose graphics processors. BMC Bioinform. 13(1), 281 (2012)CrossRefzbMATHGoogle Scholar
  4. 4.
    Ceci, M., Pio, G., Kuzmanovski, V., Dẑeroski, S.: Semi-supervised multi-view learning for gene network reconstruction. PLOS ONE 10(12), 1–27 (2015)CrossRefGoogle Scholar
  5. 5.
    Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Hempel, S., Koseska, A., Nikoloski, Z., Kurths, J.: Unraveling gene regulatory networks from time-resolved gene expression data - a measures comparison study. BMC Bioinform. 12(1), 292 (2011)CrossRefGoogle Scholar
  7. 7.
    Hsu, H.T.: An algorithm for finding a minimal equivalent graph of a digraph. J. ACM 22(1), 11–16 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Itani, S., Ohannessian, M., Sachs, K., Nolan, G.P., Dahleh, M.A.: Structure learning in causal cyclic networks. In: Proceedings of the International Conference on Causality: Objectives and Assessment, COA 2008, vol. 6, pp. 165–176. (2008)Google Scholar
  9. 9.
    Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence, 2nd edn. CRC Press Inc., Boca Raton (2010)zbMATHGoogle Scholar
  10. 10.
    Lo, L., Wong, M., Lee, K., Leung, K.: Time delayed causal gene regulatory network inference with hidden common causes. PLOS ONE 10(9), 1–47 (2015)CrossRefGoogle Scholar
  11. 11.
    Margolin, A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R., Califano, A.: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 7(Suppl 1), S7 (2006)CrossRefGoogle Scholar
  12. 12.
    Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, New York (2000)zbMATHGoogle Scholar
  13. 13.
    Penfold, C.A., Wild, D.L.: How to infer gene networks from expression profiles, revisited. Interface Focus 1(6), 857–870 (2011)CrossRefGoogle Scholar
  14. 14.
    Pinna, A., Soranzo, N., de la Fuente, A.: From knockouts to networks: establishing direct cause-effect relationships through graph analysis. PLOS ONE 10(5), e12912 (2010)CrossRefGoogle Scholar
  15. 15.
    Pio, G., Ceci, M., Malerba, D., D’Elia, D.: ComiRNet: a web-based system for the analysis of miRNA-gene regulatory networks. BMC Bioinform. 16(9), S7 (2015)CrossRefGoogle Scholar
  16. 16.
    Van den Bulcke, T., Van Leemput, K., Naudts, B., van Remortel, P., Ma, H., Verschoren, A., De Moor, B., Marchal, K.: SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinform. 7, 43 (2006)CrossRefGoogle Scholar
  17. 17.
    Zitnik, M., Zupan, B.: Data imputation in epistatic MAPs by network-guided matrix completion. J. Computat. Biol. 22(6), 595–608 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gianvito Pio
    • 1
    Email author
  • Michelangelo Ceci
    • 1
  • Francesca Prisciandaro
    • 1
  • Donato Malerba
    • 1
  1. 1.Department of Computer ScienceUniversity of Bari Aldo MoroBariItaly

Personalised recommendations