Incremental – Adaptive – Knowledge Based – Learning for Informative Rules Extraction in Classification Analysis of aGvHD

  • Maurizio Fiasché
  • Anju Verma
  • Maria Cuzzola
  • Francesco C. Morabito
  • Giuseppe Irrera
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 363)


Acute graft-versus-host disease (aGvHD) is a serious systemic complication of allogeneic hematopoietic stem cell transplantation (HSCT) that occurs when alloreactive donor-derived T cells recognize host-recipient antigens as foreign. The early events leading to GvHD seem to occur very soon, presumably within hours from the graft infusion. Therefore, when the first signs of aGvHD clinically manifest, the disease has been ongoing for several days at the cellular level, and the inflammatory cytokine cascade is fully activated. So, it comes as no surprise that to identify biomarker signatures for approaching this very complex task is a critical issue. In the past, we have already approached it through joint molecular and computational analyses of gene expression data proposing a computational framework for this disease. Notwithstanding this, there aren’t in literature quantitative measurements able to identify patterns or rules from these biomarkers or from aGvHD data, thus this is the first work about the issue. In this paper first we have applied different feature selection techniques, combined with different classifiers to detect the aGvHD at onset of clinical signs, then we have focused on the aGvHD scenario and in the knowledge discovery issue of the classification techniques used in the computational framework.


EFuNN gene selection GvHD machine learning wrapper 


  1. 1.
    Fiasché, M., Cuzzola, M., Irrera, G., Iacopino, P., Morabito, F.C.: Advances in Medical Decision Support Systems for Acute Graft-versus-Host Disease: Molecular and Computational Intelligence Joint Approaches. In: Frontiers in Biology. Higher Education Press and Springer -Verlag GmbH, doi:10.1007/s11515-011-1124-8Google Scholar
  2. 2.
    Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach, 2nd edn. Springer, London (2007)MATHGoogle Scholar
  3. 3.
    Weisdorf, D.: Graft vs. Host disease: pathology, prophylaxis and therapy: GvHD overview. Best Pr. & Res. Cl. Haematology 21(2), 99–100 (2008)CrossRefGoogle Scholar
  4. 4.
    Ferrara, J.L.: Advances in the clinical management of GvHD. Best Pr. & Res. Cl. Haematology 21(4), 677–682 (2008)CrossRefGoogle Scholar
  5. 5.
    Fiasché, M., Cuzzola, M., Iacopino, P., Kasabov, N., Morabito, F.C.: Personalized Modeling based Gene Selection for acute GvHD Gene Expression Data Analysis: a Computational Framework Proposed. Australian Journal of Intelligent Information Processing Systems 12(4) (2010), Machine Learning Applications (Part II)Google Scholar
  6. 6.
    Langley, P.: Selection of relevant features in machine learning. In: Proceedings of AAAI Fall Symposium on Relevance, pp. 140–144 (1994)Google Scholar
  7. 7.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)MATHGoogle Scholar
  8. 8.
    Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. Thesis. Department of Computer Science, University of Waikato, New Zealand Google Scholar
  9. 9.
    Fiasché, M., Verma, A., Cuzzola, M., Iacopino, P., Kasabov, N., Morabito, F.C.: Discovering Diagnostic Gene Targets for Early Diagnosis of Acute GvHD Using Methods of Computational Intelligence on Gene Expression Data. Journal of Artificial Intelligence and Soft Computing Research 1(1), 81–89 (2011)Google Scholar
  10. 10.
    Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods–Support Vector Learning. MIT Press, Cambridge (1998)Google Scholar
  11. 11.
    Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.W., Schummer, M., Haussler, D.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906–914 (2000)CrossRefGoogle Scholar
  12. 12.
    Robins, A.: Consolidation in neural networks and the sleeping brain. Connection Sci. 8(2), 259–275 (1996)CrossRefGoogle Scholar
  13. 13.
    Duch, W., Adamczak, R., Grabczewski, K.: Extraction of logical rules from neural networks. Neural Proc. Lett. 7, 211–219 (1998)CrossRefGoogle Scholar
  14. 14.
    Jang, R.: ANFIS: Adaptive network based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Kasabov, N., Kim, J.S., Watts, M., Gray, A.: FuNN/2 - A fuzzy neural network architecture for adaptive learning and knowledge acquisition. Inf. Sci. Appl. 101(3-4), 155–175 (1997)CrossRefGoogle Scholar
  16. 16.
    Yamakawa, T., Uchino, E., Miki, T., Kusanagi, H.: A neo fuzzy neuron and its application to system identification and prediction of the system behaviour. In: Proceedings of the Second International Conference on Fuzzy Logic & Neural Networks, Iizuka, Japan, pp. 477–483 (1992)Google Scholar
  17. 17.
    Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Trans. Evolutionary Computation 3(4), 287–297 (1999)CrossRefGoogle Scholar
  18. 18.
    Fiasché, M., Cuzzola, M., Fedele, R., Iacopino, P., Morabito, F.C.: Machine Learning and Personalized Modeling based Gene Selection for acute GvHD Gene Expression Data Analysis. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds.) ICANN 2010, Part I. LNCS, vol. 6352, pp. 217–223. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Foley Jason, J.E., Mariotti, J., Ryan, K., Eckhaus, M., Fowler, D.H.: The cell therapy of established acute graft-versus-host disease requires IL-4 and IL-10 and is abrogated by IL-2 or host-type antigen-presenting cells. Biology of Blood and Marrow Transplantation 14, 959–972 (2008)CrossRefGoogle Scholar
  20. 20.
    Paczesny, S., Hanauer, D., Sun, Y., Reddy, P.: New perspectives on the biology of acute GvHD. Bone Marrow Transplantation, 45-1–45-11 (2010)Google Scholar
  21. 21.
    Kasabov, N.: Global, local and personalised modelling and profile discovery in Bioinformatics: An integrated approach. Pattern Recognition Letters 28(6), 673–685 (2007)CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2011

Authors and Affiliations

  • Maurizio Fiasché
    • 1
    • 2
  • Anju Verma
    • 2
  • Maria Cuzzola
    • 2
  • Francesco C. Morabito
    • 1
  • Giuseppe Irrera
    • 2
  1. 1.DIMETUniversity “Mediterranea” of Reggio CalabriaItaly
  2. 2.Transplant Regional Center of Stem Cells and Cellular Therapy, ”A. Neri”Reggio CalabriaItaly

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