Bio-medical Ontologies Maintenance and Change Management

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210 I.R. Godínez et al. 31. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository.University of California, Department of Information and Computer Science, Irvine, CA (2007), http://www.ics.uci.edu/mlearn/MLRepository.html 32. Brunak, S., Engelbrecht, J., Knudsen, S.: Prediction of human mRNA donor and acceptor sites from the DNA sequence. J. Mol. Biol. 220, 49–65 (1991) 33. Ortega, J.: On the informativeness of the DNA promoter sequences domain theory. Journal of Artificial Intelligence Research 2, 361–367 (1995) 34. Baffes, P.T., Mooney, R.J.: Symbolic revision of theories with M-of-N rules. In: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France (1993) 35. Rampone, S.: Recognition of splice junction on DNA sequences by BRAIN learning algorithm. Bioinformatics 14(8), 676–684 (1998)
Mining Clinical, Immunological, and Genetic Data of Solid Organ Tansplantation Michele Berlingerio 1,2 , Francesco Bonchi 2 , Michele Curcio 3 , Fosca Giannotti 2 , and Franco Turini 4 1 IMT School for Advanced Studies Via San Micheletto, 3 - Lucca, Italy 2 Pisa KDD Laboratory ISTI - CNR, Area della Ricerca di Pisa Via Giuseppe Moruzzi, 1 - Pisa, Italy 3 Unità di Immunoematologia 2, Azienda Ospedaliero Universitaria Pisana Via Paradisa, 2 - Pisa, Italy 4 Pisa KDD Laboratory - Computer Science Dept., University of Pisa Largo Pontecorvo, 3 - Pisa, Italy Summary. Clinical databases store large amounts of information about patients and their medical conditions. Data mining techniques can extract relationships and patterns implicit in this wealth of data, and thus be helpful in understanding the progression of diseases and the efficacy of the associated therapies. In this perspective, in Pisa (Italy) we have started an important data collection and analysis project, where a very large number of epidemiological, clinical, immunological and genetic variables collected before the transplantation of a solid organ, and during the follow-up assessment of the patients, are stored in a datawarehouse for future mining. This on-going data collection involves all liver, kidney, pancreas and kidney-pancreas transplantations of the last five years of one of the largest (as to number of transplantations) centers in Europe. The project ambitious goal is to gain deeper insights in all the phenomena related to solid organ transplantation, with the aim of improving the donor-recipient matching policy used nowadays. In this chapter we report in details two different data mining activities developed within this project. The first analysis involves mining genetic data of patients affected by terminal hepatic cirrhosis with viral origin (HCV and HBV) and patients with terminal hepatic cirrhosis with non-viral origin (autoimmune): the goal is to assess the influence of the HLA antigens on the course of the disease. In particular, we have evaluated if some genetic configurations of the class I and class II HLA are significantly associated with the triggering causes of the hepatic cirrhosis. The second analysis involves clinical data of a set of patients in the follow-up of a liver transplantation. The aim of the data analysis is that of assessing the effectiveness of the extracorporeal photopheresis (ECP) as a therapy to prevent rejection in solid organ transplantation. For both analyses we describe in details, the medical context and goal, the nature and structure of the data. We also discuss which kind of data mining technique is the most suitable for our purposes, and we describe the details of the knowledge discovery process followed and extracted knowledge. A.S. Sidhu et al. (Eds.): Biomedical Data and Applications, SCI 224, pp. 211–236. springerlink.com c○ Springer-Verlag Berlin Heidelberg 2009
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Amandeep S. Sidhu and Tharam S. Dil
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Amandeep S. Sidhu and Tharam S. Dil
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Preface The molecular biology commu
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Preface VII biomedical systems, and
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X Contents Mining Clinical, Immunol
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2 A.S. Sidhu, M. Bellgard, and T.S.
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Towards Bioinformatics Resourceomes
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2 The New Waves Towards Bioinformat
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2.4 Semantic Web Towards Bioinforma
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A Summary of Genomic Databases: Ove
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Appendix A Summary of Genomic Datab
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Protein Data Integration Problem Am
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Protein Data Integration Problem 57
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Fig. 3. Class Hierarchy of Protein
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Bio-medical Ontologies Maintenance
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TextToOnto (Maedche and Volz 2001)
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Substructure Analysis of Metabolic
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Completing the Total Wellbeing Puzz
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Genetic Algorithm in Ab Initio Prot
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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