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Using Rules of Thumb for Repairing Inconsistent Answer Set Programs

  • Elie MerhejEmail author
  • Steven Schockaert
  • Martine De Cock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9310)

Abstract

Answer set programming is a form of declarative programming that can be used to elegantly model various systems. When the available knowledge about these systems is imperfect, however, the resulting programs can be inconsistent. In such cases, it is of interest to find plausible repairs, i.e. plausible modifications to the original program that ensure the existence of at least one answer set. Although several approaches to this end have already been proposed, most of them merely find a repair which is in some sense minimal. In many applications, however, expert knowledge is available which could allow us to identify better repairs. In this paper, we analyze the potential of using expert knowledge in this way, by focusing on a specific case study: gene regulatory networks. We show how we can identify the repairs that best agree with insights about such networks that have been reported in the literature, and experimentally compare this strategy against the baseline strategy of identifying minimal repairs.

References

  1. 1.
    Arenas, M., Bertossi, L., Chomicki, J.: Answer sets for consistent query answering in inconsistent databases. Theory Pract. Logic Program. 3, 393–424 (2003)zbMATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    Arieli, O., Denecker, M., Van Nuffelen, B., Bruynooghe, M.: Database repair by signed formulae. In: Seipel, D., Turull-Torres, J.M. (eds.) FoIKS 2004. LNCS, vol. 2942, pp. 14–30. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  3. 3.
    Berntenis, N., Ebeling, M.: Detection of attractors of large boolean networks via exhaustive enumeration of appropriate subspaces of the state space. BMC Bioinform. 14(1), 361 (2013)CrossRefGoogle Scholar
  4. 4.
    Chen, T., Filkov, V., Skiena, S.S.: Identifying gene regulatory networks from experimental data. Parallel Comput. 27(1), 141–162 (2001)zbMATHCrossRefGoogle Scholar
  5. 5.
    Cohen, R., Havlin, S.: Scale-free networks are ultrasmall. Phys. Rev. Lett. 90(5), 058701 (2003)CrossRefGoogle Scholar
  6. 6.
    De Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1), 67–103 (2002)CrossRefGoogle Scholar
  7. 7.
    Delgrande, J.P., Schaub, T.: A consistency-based approach for belief change. Artif. Intell. 151(1), 1–41 (2003)zbMATHMathSciNetCrossRefGoogle Scholar
  8. 8.
    Ellis, J.J., Kobe, B.: Predicting protein kinase specificity: predikin update and performance in the DREAM4 challenge. PLoS One 6(7), e21169 (2011)CrossRefGoogle Scholar
  9. 9.
    Fayruzov, T., Janssen, J., Vermeir, D., Cornelis, C.: Modelling gene and protein regulatory networks with answer set programming. Int. J. Data Mining Bioinform. 5(2), 209–229 (2011)CrossRefGoogle Scholar
  10. 10.
    Gebser, M., Guziolowski, C., Ivanchev, M., Schaub, T., Siegel, A., Thiele, S., Veber, P.: Repair and prediction (under inconsistency) in large biological networks with answer set programming. In: KR (2010)Google Scholar
  11. 11.
    Gebser, M., König, A., Schaub, T., Thiele, S., Veber, P.: The BioASP library: ASP solutions for systems biology. In: ICTAI (1), pp. 383–389 (2010)Google Scholar
  12. 12.
    Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: ICLP/SLP, vol. 88, pp. 1070–1080 (1988)Google Scholar
  13. 13.
    Kauffman, S.A.: The Origins of Order: Self-organization and Selection in Evolution. Oxford University Press, Oxford (1993)Google Scholar
  14. 14.
    Lau, K.Y., Ganguli, S., Tang, C.: Function constrains network architecture and dynamics: a case study on the yeast cell cycle boolean network. Phys. Rev. E 75(5), 051907 (2007)CrossRefGoogle Scholar
  15. 15.
    Lee, T.I., Rinaldi, N.J., Robert, F., Odom, D.T., Bar-Joseph, Z., Gerber, G.K., Hannett, N.M., Harbison, C.T., Thompson, C.M., Simon, I., et al.: Transcriptional regulatory networks in saccharomyces cerevisiae. Science 298(5594), 799–804 (2002)CrossRefGoogle Scholar
  16. 16.
    Li, F., Long, T., Lu, Y., Ouyang, Q., Tang, C.: The yeast cell-cycle network is robustly designed. Proc. Natl. Acad. Sci. U.S.A. 101(14), 4781–4786 (2004)CrossRefGoogle Scholar
  17. 17.
    Lifschitz, V.: Action languages, answer sets, and planning. In: Apt, K.R., et al. (eds.) The Logic Programming Paradigm, pp. 357–373. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  18. 18.
    Lifschitz, V.: Answer set programming and plan generation. Artif. Intell. 138(1), 39–54 (2002)zbMATHMathSciNetCrossRefGoogle Scholar
  19. 19.
    Lifschitz, V.: What is answer set programming? In: AAAI, vol. 8, pp. 1594–1597 (2008)Google Scholar
  20. 20.
    Menéndez, P., Kourmpetis, Y.A., ter Braak, C.J., van Eeuwijk, F.A.: Gene regulatory networks from multifactorial perturbations using graphical lasso: application to the DREAM4 challenge. PloS One 5(12), e14147 (2010)CrossRefGoogle Scholar
  21. 21.
    Merhej, E., Schockaert, S., De Cock, M., Blondeel, M., Alfarone, D., Davis, J.: Repairing inconsistent taxonomies using map inference and rules of thumb. In: Proceedings of the 5th International Workshop on Web-scale Knowledge Representation Retrieval & Reasoning, pp. 31–36. ACM (2014)Google Scholar
  22. 22.
    Mobilia, N., Rocca, A., Chorlton, S., Fanchon, E., Trilling, L.: Logical modeling and analysis of regulatory genetic networks in a non monotonic framework. In: Ortuño, F., Rojas, I. (eds.) IWBBIO 2015, Part I. LNCS, vol. 9043, pp. 599–612. Springer, Heidelberg (2015) Google Scholar
  23. 23.
    Mushthofa, M., Torres, G., Van de Peer, Y., Marchal, K., De Cock, M.: ASP-G: an ASP-based method for finding attractors in genetic regulatory networks. Bioinformatics 30(21), 3086–3092 (2014)CrossRefGoogle Scholar
  24. 24.
    Shmulevich, I., Dougherty, E.R., Zhang, W.: From boolean to probabilistic boolean networks as models of genetic regulatory networks. Proc. IEEE 90(11), 1778–1792 (2002)CrossRefGoogle Scholar
  25. 25.
    Singla, P., Kautz, H., Luo, J., Gallagher, A.: Discovery of social relationships in consumer photo collections using markov logic. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–7. IEEE (2008)Google Scholar
  26. 26.
    Wernicke, S., Rasche, F.: Fanmod: a tool for fast network motif detection. Bioinformatics 22(9), 1152–1153 (2006)CrossRefGoogle Scholar
  27. 27.
    Yang, L., Meng, Y., Bao, C., Liu, W., Ma, C., Li, A., Xuan, Z., Shan, G., Jia, Y.: Robustness and backbone motif of a cancer network regulated by miR-17-92 cluster during the G1/S transition. PloS One 8(3), e57009 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Elie Merhej
    • 1
    Email author
  • Steven Schockaert
    • 2
  • Martine De Cock
    • 1
    • 3
  1. 1.Ghent UniversityGhentBelgium
  2. 2.Cardiff UniversityCardiffUK
  3. 3.University of Washington TacomaTacomaUSA

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