Mining interesting clinico-genomic associations: The HealthObs approach

IFIP International Federation for Information Processing

Volumn 247, Issue , 2007, Pages 137-145

  Potamias, George ^a   Koumakis, Lefteris ^a   Kanterakis, Alexandros ^a   Moustakis, Vassilis ^a,c   Kafetzopoulos, Dimitrsi ^b   Tsiknakis, Manolis ^a  

^a INSTITUTE OF COMPUTER SCIENCE   (Greece)

^b INSTITUTE OF MOLECULAR BIOLOGY AND BIOTECHNOLOGY   (Greece)

^c TECHNICAL UNIVERSITY OF CRETE   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; GENE EXPRESSION; SEMANTICS; XML;

ASSOCIATION RULES MINING; DATA-MINING TECHNIQUES; GENOMIC DATA; INTELLIGENT PROCESSING; SEAMLESS INTEGRATION;

ARTIFICIAL INTELLIGENCE;

EID: 36248955965     PISSN: 15715736     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-0-387-74161-1_15     Document Type: Conference Paper

References (21)

1. A. Amir, R. Feldman R., and R. Kashi, A new and versatile method for association generation, Information Systems 2, 333-347, (1997).
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).
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).
4. F. Cardoso, Microarray technology and its effect on breast cancer (re)classification and prediction of outcome, Breast Cancer Res., 5, 303-304, (2003).
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.
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.
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).
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).
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.
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).
11. J. Grimson, Delivering the electronic healthcare record of the 21st century, International Journal of Medical Informatics 64, pp. 111-127 (2001).
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.
13. L.J. van 't Veer, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, pp. 530-536 (2002).
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).
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).
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).
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).
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.
19. R.J. Jr. Bayardo, Efficiently mining long patterns from databases, SIGMOD Record 27(2) pp. 85-93 (1998).
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).
21. S.K. Gruvberger, M.Ringnér, P. Eden, A. Borg, M. Femo, 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).