Bio-medical Ontologies Maintenance and Change Management

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222 M. Berlingerio et al. Table 2. Some interesting patterns discovered ID Pattern Disease patients DB control DB ratio 1 Sex=F; A =1;B =35 autoim. 1.463% 0.169% 8.656 2 Age in [56,60]; A =2;B =11 autoim. 1.219% 0% +∞ 3 A1 =1;A2 =2;B = 18; DRB1 =11 autoim. 1.463% 0.169% 8.656 4 Sex=M; A =1;B = 18; DRB1 =11 autoim. 1.463% 0.169% 8.656 5 Age in [51,55]; A =1;B = 35; DRB1 =11 any 1.219% 0.233% 5.231 6 Sex=M; A =2;DRB11 = DRB12 =11 autoim. 1.463% 0.254% 5.759 7 Sex=M; A =2;B =51 HCV+, autoim. 2.439% 0.701% 3.476 8 Sex=M; A =2;B = 51; DRB1 =11 autoim. 1.463% 0.445% 3.287 9 Age in [56,60]; A =1;B = 18; DRB1 =11 HBV+ 1.219% 0% +∞ 10 Sex=F; Age in [41,45]; A =2;B = 18; DRB1 =11 any 1.219% 0.042% 29.023 11 Age in [56,60]; A =2;DRB1 =7 HCV+, autoim. 1.951% 0% +∞ database, whose relative frequency is given in the fourth column. In the fifth column we have the relative frequency of the pattern (without the disease information) in the control database. In the last column is reported the ratio between the relative frequency in the patients and the relative frequency in the control database. The higher this ratio, the more interesting the pattern. The patterns exhibiting a surprisingly high ratio, automatically selected in our post-processing phase, have been then evaluated by the domain experts. 4.4 Patterns Evaluation In the following we report some bio-medical interpretation of a few patterns which have been considered particularly interesting by the domain experts. Patterns 3 and 4: these two patterns, sharing the common haplotype A=1, B=18, DRB1=11, exhibit a very high ratio w.r.t. the control population. This could be explained by assuming that, as reported for some autoimmune diseases (Grave’s disease, Hashimoto’s disease), particular alleles (HLA-DRB1 = 11 in our case) have a higher capability in presenting the antigens to T cells. Patterns 7 and 8: these two patterns share the subpattern A=2, B=51 with autoimmune cirrhosis. In the biological literature the association among A=2, B=51 and the hepatic cirrhosis with autoimmune origin was already known (see [29] and [30]). The discovery of these patterns confirms the adequateness of the proposed methodology. Pattern 11: describes a set of patients 56 to 60 years old, with A = 2, DRB1 = 7, with hepatic cirrhosis from viral HCV+ infection on hepatic cirrhosis with autoimmune origin. Such a pattern has a relative frequency of 1,951% in the patients database, while it never appears in the much larger control database. This situation caught our attention and we further analyzed it. Surprisingly, we discovered that while no individual older than 55 is present in the healthy population, we found a 2,607% of healthy individuals with the same haplotype (A = 2, DRB1 = 7), but younger than 55. This could point out that patients with such haplotype are not capable to eliminate the HCV+ virus but they are predisposed to the
Mining Clinical, Immunological, and Genetic Data 223 development of a chronic cirrhosis, leading to transplantation or to death. This would explain why over a certain age threshold, no individual is present in the healthy population. The obtained results showed that the HLA antigens connected to high level hepatic damage are different in accordance to the cause of the disease. The pattern discovery techniques we used proved their capability in bringing to light particular associations in the haplotypic setting that could remain hidden to the traditional data analysis methods used in biomedicine. Another interesting aspect acknowledged by the domain experts is that the HLA pattern discovered are, indeed, haplotypes, while the input data were containing genotypes (genotype is the entire allelic combination of an individual, while the haplotype is the allelic sequence inherited as a block from one of the parents). This fact, on one hand strengthens the biological significance of the patterns obtained, on the other hand, it suggests that frequent pattern discovery techniques can be applied to the haplotype inference problem, i.e., the problem of reconstructing haplotypes of individuals, given their genotypes. This problem has been widely studied in bioinformatics, and to date is still a hot algorithmic problem [5, 11, 10, 12]. 5 Mining Temporal Patterns Assessing the Effectiveness of a Therapy A typical structure of medical data is a sequence of observations of clinical parameters taken at different time moments. In this kind of contexts, the temporal dimension of data is a fundamental variable that should be taken in account in the mining process and returned as part of the extracted knowledge. Therefore, the classical and well established framework of sequential pattern mining is not enough, because it only focuses on the sequentiality of events, without extracting the typical time elapsing between two particular events. Time-annotated sequences (TAS ), is a novel mining paradigm that solves this problem. Recently defined in our laboratory together with an efficient algorithm for extracting them, TAS are sequential patterns where each transition between two events is annotated with a typical transition time that is found frequent in the data. In principle, this form of pattern is useful in several contexts: for instance, (i) in web log analysis, different categories of users (experienced vs. novice, interested vs. uninterested, robots vs. humans) might react in similar ways to some pages - i.e., they follow similar sequences of web access - but with different reaction times; (ii) in medicine, reaction times to patients’ symptoms, drug assumptions and reactions to treatments are a key information. In all these cases, enforcing fixed time constraints on the mined sequences is not a solution. It is desirable that typical transition times, when they exist, emerge from the input data. TAS patterns have been also used as basic brick to build a truly spatio-temporal trajectory pattern mining framework [9].
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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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Extraction of Constraints from Biol
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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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