Bio-medical Ontologies Maintenance and Change Management

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214 M. Berlingerio et al. and to the fact that tipically the physicians miss the analytical instruments needed to explore all the possible combinations of such variables. In this context we have started a collaboration between the “Unità di Immunoematologia 2” and the Knowledge Discovery and Delivery Laboratory of the ISTI - CNR (National Council of Research), aimed at analyzing by means of data mining techniques the information collected about patients who had a solid organ transplant, with the final goal of extracting knowledge useful for understanding the genetical mechanisms involved in the transplant success. This collaboration as been named the Health Mine project, and it as been structured in three subprojects, reflecting different speed in the data collection phase, and different challenges in the data analysis, thus shaping short term and long-term objectives: 1. HLA and liver diseases, 2. Photopheresys assessment, 3. HLA and kidney transplantation. HLA and liver diseases. Here the medical problem is to find possible associations between HLA alleles combinations and serious liver diseases that lead the patients to organ transplantation. The main objective of this analysisistofindpatterns involving DNA, gender, age and other variables, which are more frequent in the patients with liver diseases than in the healthy population. In this way we can make hypotheses on possible associations between particular genetic and demographics characteristics and particular kinds of cirrhosis. In this analysis we used Frequent Pattern Discovery techniques, as further explained in Section 4. Photopheresys assessment. In the last few years, a new type of therapy, the extracorporeal photopheresys (ECP) has started emerging as a helpful therapy when the reject is detected. This treatment can reduce the amount of lymphocytes that sustain the reject process. The objective of this data mining activity is to assess the efficacy of the photopheresys in patients with liver transplant, by analyzing at the same time several kinds of genetical and biochemical variables, in order to individuate which of them can positively influence the success of the photopheretic therapy. HLA and kidney transplantation. The chimerism and the microchimerism are phenomena that can occur in the recipient after a transplant. In poor words, they consist in the acquisition, by the recipient’s body, of some cells of the donor, due to the organ transplantation. In particular, in this analysis we focus on kidney transplantation, and the objectives are: • to create a database containing all the clinical variables collected during the follow-up of the recipients, • to set-up a technique for assessing both from the qualitative and the quantitative point of view the systemic microchimerism by using the HLA polymorphism and the Real-Time technology,
Mining Clinical, Immunological, and Genetic Data 215 • to assess the relationship among epidemiological, clinical, genetical and immunological variables w.r.t. the study of the polymorphism of the minor histocompatibility antigens. In the next section we discuss the challenges related to the data collection phase which is still in progress. While in section 4 and 5 we present the two analysis for which the data collection and mining has been concluded in terms of applied methodologies, results and patterns evaluation. 3 Data Collection The data collection phase in the medical domain is inherently much more challenging that in other domains. It is the nature of the data itself that makes it on the one hand very difficult to be collected (and in reasonably size), and on the other hand very precious for the kind of knowledge that we can acquire by analyzing it. In particular, the reasons behind the hardness of medical data collection are various: • Privacy and legal constraints: due to the highly sensitive nature of the data, the outsourcing of the data mining analysis to researchers external to the medical institution is usually quite complex or in some cases almost impossible. On the one hand the data can hardly be moved outside the medical institution, on the other hand external researcher can be hardly put at work inside the medical institution. • Ownership of the data: it is very difficult to access the medical data owned by different laboratories, unless they do not cooperate in the same research project, and sometime even if they do. • Money-cost of data acquisition: most of the data come from results of expensive chemical-clinical analyses that are not always possible to perform. • Time-cost of data acquisition: there are no machines that take the blood from the refrigerator, perform automatically the needed analyses, report the results in a standard form, sort, merge and clean the data, and so on. All these operations have to be done manually by people that are not fulltime dedicated to this process. Sometimes already existing information are not stored digitally, but physically written on paper, thus even more difficult to access, understand and acquire. For the reasons above, the most involving data collection, i.e., the one for the third subproject (HLA and kidney transplantation) is still going on, while for the other two subprojects is finished. For the first analysis the database contained 2 datasets: the first one containing data from 534 patients with autoimmune or viral cirrhosis, collected since 1996. For each of the patients information about their age, gender, geographic origin and the alleles corresponding to the six HLA antigens corresponding to loci A, B and DRB1, plus some information about the disease are collected. In the second dataset, used as control population, we had data
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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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Page 167 and 168: Bio-medical Ontologies Maintenance
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Completing the Total Wellbeing Puzz
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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