Mining Interesting Clinico-Genomic Associations: The HealthObs Approach

  • George Potamias
  • Lefteris Koumakis
  • Alexandros Kanterakis
  • Vassilis Moustakis
  • Dimitrsi Kafetzopoulos
  • Manolis Tsiknakis
Part of the IFIP The International Federation for Information Processing book series (IFIPAICT, volume 247)


HealthObs is an integrated (Java-based) environment targeting the seamless integration and intelligent processing of distributed and heterogeneous clinical and genomic data. Via the appropriate customization of standard medical and genomic data-models HealthObs achieves the semantic homogenization of remote clinical and gene-expression records, and their uniform XML-based representation. The system utilizes data-mining techniques (association rules mining) that operate on top of query-specific XML documents. Application of HealthObs on a real world breast-cancer clinico-genomic study demonstrates the utility and efficiency of the approach.


Association Rule Association Rule Mining Electronic Healthcare Record Semantic Homogenization Linked Sample 


  1. 1.
    A. Amir, R. Feldman R., and R. Kashi, A new and versatile method for association generation, Information Systems 2, 333–347, (1997).CrossRefGoogle Scholar
  2. 2.
    A. Analyti, H. Kondylakis, D. Manakanatas, M. Kalaitzakis, D. Plexousakis, and G. Potamias, Integrating Clinical and Genomic Information through the PrognoChip Mediator, Lecture Notes in Bioinformatics 4345, 250–261, (2006).Google Scholar
  3. 3.
    D. Kanterakis, G. Potamias, Supporting Clinico-Genomic Knowledge Discovery: A Multistrategy Data Mining Process, Lecture Notes in Computer Science 3955, pp. 520–524 (2006).Google Scholar
  4. 4.
    F. Cardoso, Microarray technology and its effect on breast cancer (re)classification and prediction of outcome, Breast Cancer Res., 5, 303–304, (2003).CrossRefGoogle Scholar
  5. 5.
    G. Potamias and V. Moustakis, Knowledge Discovery from Distributed Clinical Data Sources: The Era for Internet-Based Epidemiology, in: 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (2001), pp. 3638–3641 vol.4.Google Scholar
  6. 6.
    G. Potamias, A. Analyti, D. Kafetzopoulos, M. Kafousi, T. Margaritis, D. Plexousakis, P. Poirazi, M. Reczko, Y. Tollis, E. Sanidas, E. Stathopoulos, M. Tsiknakis, and S. Vassilaros, Breast Cancer and Biomedical Informatics: The PrognoChip Project, in: Proceedings of the 17th IMACS World Congress Scientific Computation, Applied Mathematics and Simulation, Paper T3-I-68-1066.Google Scholar
  7. 7.
    G. Potamias, D. Kafetzopoulos, and M. Tsiknakis, Integrated Clinico-Genomics Environment: Design and Operational Specification, Journal for Quality of Life Research (JQLR), 2(1), pp. 145–150 (2004).Google Scholar
  8. 8.
    G. Potamias, L. Koumakis, and V. Moustakis, Mining XML Clinical Data: the HealthObs System. Ingénierie des Systèmes d’Information 10(1), pp. 59–79 (2005).CrossRefGoogle Scholar
  9. 9.
    H. Mannila, H. Toivonen, and A.I. Verkamo, Efficient algorithms for discovering association rules, in: KDD-94: AAAI Workshop on Knowledge Discovery in Databases, (2001), pp. 181–192, 1994.Google Scholar
  10. 10.
    H.P. Eich, G. de la Calle, C. Diaz, S. Boyer, A.S. Pena, B.G. Loos, P. Ghazal, and I. Bernstein, Practical Approaches to the Development of Biomedical Informatics: the INFOBIOMED Network of Excellence. Stud Health Technol Inform., 116, pp. 39–44, (2005).Google Scholar
  11. 11.
    J. Grimson, Delivering the electronic healthcare record of the 21st century, International Journal of Medical Informatics 64, pp. 111–127 (2001).CrossRefGoogle Scholar
  12. 12.
    L. Koumakis, HealthObs: Health Observatory. An integrated system of data mining and knowledge discovery over distributed and heterogeneous clinical sources, Department of Computer Science, University of Crete MSc thesis (in Greek), 2004.Google Scholar
  13. 13.
    L.J. van’ t Veer, et al., Gene expression profiling predicts clinical outcome of breast cancer, Nature 415, pp. 530–536 (2002).CrossRefGoogle Scholar
  14. 14.
    M. May, G. Potamias, and S. Rüping, Grid-based Knowledge Discovery in Clinico Genomic Data, Lecture Notes in Bioinformatics 4345, pp. 219–230 (2006).Google Scholar
  15. 15.
    M. Tsiknakis, D. Kafetzopoulos, G. Potamias, A. Analyti, K. Marias, and A. Manganas, Building a European biomedical grid on cancer: the ACGT Integrated Project, Stud Health Technol Inform., 120, pp. 247–258, (2006).Google Scholar
  16. 16.
    M. Tsiknakis, D. Katehakis and, S. Orphanoudakis, An open, component-based information infrastructure for integrated health information networks, International Journal of Medical Informatics 68(1–3), pp. 3–26 (2002).CrossRefGoogle Scholar
  17. 17.
    O.M. San, V. Huynh, and Y. Nakamori, An alternative extension of the k-means algorithm for clustering categorical data, Int. J. Appl. Math. Comput. Sci. 14(2), pp. 241–247(2004).MATHMathSciNetGoogle Scholar
  18. 18.
    R. Agrawal, H. Manilla, R. Srikant, H. Toivonen, and I. A. Verkamo, Fast discovery of association rules, in: Advances in Knowledge Discovery and Data Mining, (AAAI/MIT Press, 1995), pp. 307–328.Google Scholar
  19. 19.
    R.J. Jr. Bayardo, Efficiently mining long patterns from databases, SIGMOD Record 27(2), pp. 85–93 (1998).CrossRefGoogle Scholar
  20. 20.
    S. Gupta, S. Rao, and V. Bhatnagar, K-means Clustering Algorithm for Categorical Attributes, Lecture Notes in Computer Science 1676, pp. 203–208 (1999).CrossRefGoogle Scholar
  21. 21.
    S.K. Gruvberger, M. Ringnér, P. Eden, A. Borg, M. Ferno, C. Peterson, and P.S Meltzer, Expression profiling to predict outcome in breast cancer: the influence of sample selection, Breast Cancer Res. 5(1), pp. 23–26 (2003).CrossRefGoogle Scholar

Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • George Potamias
    • 1
  • Lefteris Koumakis
    • 1
  • Alexandros Kanterakis
    • 1
  • Vassilis Moustakis
    • 1
    • 3
  • Dimitrsi Kafetzopoulos
    • 2
  • Manolis Tsiknakis
    • 1
  1. 1.Institute of Computer Science (ICS)Foundation for Research & Technology — Hellas (FORTH)Heraklion, CreteGreece
  2. 2.Institute of Molecular Biology & Biotechnology (IMBB)Foundation for Research & Technology — Hellas (FORTH)Heraklion, CreteGreece
  3. 3.Department of Production Engineering & ManagementTechnical University of CreteChania, CreteGreece

Personalised recommendations