Bio-medical Ontologies Maintenance and Change Management

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218 M. Berlingerio et al. hepatic cirrhosis (e.g., HCV, HBV), the genetical setting of the host patient seems to play a central role. The first genes that have been studied, due to their important function in controlling the immune response, were the class I and class II HLA gene. The association between the class II HLA molecules (HLA-DRB1, HLA-DQB1) and, mainly, the chronic infection by HCV and HBV has firstly been reported in [27, 26]. However, the HLA has also an important role in determining the susceptibility to autoimmune based diseases. Most of the diseases associated with the HLA system are, in fact, pathologies related to an autoimmune response against self antigens: in practice, in some genetically predisposed individuals could start some uncontrollable immune phenomena, direct against self antigens that determine a physiologic damage to the target organ. While some associations between liver diseases due to viral infections and HLA antigens are known in literature, it is still not clear which role is played by which antigens in the development of autoimmune hepatic cirrhosis. It is thus important to assess the existence of associations between haplotypic settings (possibly mixed with clinical and demographical information) and hepatic cirrhosis. Algorithms developed for frequent patterns discovery can help us achieve this goal. Thanks to their ability in handling a very large number of variables and the associated exponential search space, such techniques can discover interesting haplotypic patterns beyond traditional analysis methods capabilities. In this section we report a frequent pattern discovery analysis on genetical 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), conducted with the aim of assessing the influence of the HLA antigens on the course of the disease. In particular, we have evaluated if some genetical configurations of the class I and class II HLA are significantly associated with the triggering causes of the hepatic cirrhosis. The analysis has been performed on two datasets: the first one contains data of 534 patients with terminal cirrhosis that led to liver transplantation, while the second one used as a control population, contains the data of 4718 healthy individuals coming from the same geographic area (this dataset has been previously used in [22]). The frequent pattern analysis has led to the discovery of some associations already known in the medical literature, and of some not previously known interesting ones, which are object of further biological investigation. The main contribution of this analysis is however methodological: it has proven the adequateness of the frequent pattern discovery methods on this kind of data. 4.2 Frequent Pattern Discovery The collation of large electronic databases of scientific and commercial information has led to a dramatic growth of interest in methods for discovering
Mining Clinical, Immunological, and Genetic Data 219 structures in such databases. These methods often go under the general name of data mining. However, recently two different kinds of structures sought in data mining have been identified: models and patterns. The first of these, models, are high level, global, descriptive summaries of data sets. Patterns, on the other hand, are local descriptive structures. Patterns may be regarded as local models, and may involve just a few points or variables; that is, they are descriptions of some small part of the data, instead of overall descriptions. Accordingly, Pattern Discovery has a distinguished role within data mining technology. In particular, since frequency provides support to any extracted knowledge, it is the most used and maybe the most useful measure of interest for the extracted patterns. Therefore during the last decade a lot of researchers have focussed their studies on the computational problem of Frequent Pattern Discovery, i.e., mining patterns which satisfy a user-defined minimum threshold of frequency [2, 13]. The simplest form of a frequent pattern is the frequent itemset. Definition 1 (Frequent Itemset Mining). Let I = {x1, ..., xn} be a set of distinct items, an itemset X is a non-empty subset of I. Atransaction database D is a bag of itemsets t ∈ 2 I , usually called transactions. The support of an itemset X in a database D, denotedsup D(X), is the number of transactions which are superset of X. Given a user-defined minimum support, denoted σ, anitemsetX is called frequent in D if sup D (X) ≥ σ. The Frequent Itemset Mining Problem requires to compute all the itemsets which are frequent in a transaction database: F(D,σ)={〈X, sup D (X)〉|X ∈ 2 I ∧ sup D (X) ≥ σ} The identification of sets of items, products, symptoms, characteristics, and so forth, that often occur together in the given database, can be seen as one of the most basic tasks in data mining. Although frequent itemsets are per se meaningful, the original motivation to extract them was given by the well known association rules analysis [1], where the analyst is interested in finding rules describing customers behavior in buying products. Their direct applicability to business problems together with their inherent understandability, even for non data mining experts, made association rules a popular mining method, and frequent itemsets mining one of the hottest research themes in data mining. However frequent itemsets are meaningful not only in the context of association rules mining: they can be used as basic elements in many other kind of analysis, and in particular, they can be used to build global models, ranging from classification [20, 19] to clustering [25, 32]. In the biological domain where our analysis applies, we are not interested in all possible frequent itemsets. Intuitively, we search for patterns describing HLA configurations and their association with the diseases, thus we are mainly interested in itemsets covering as many as possible of the variables regarding HLA loci and the other variables. In practice, if we got a pattern (a set of characteristics) {a, b, c} which has the same frequency of a larger
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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 171 and 172: Bio-medical Ontologies Maintenance
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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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