Bio-medical Ontologies Maintenance and Change Management

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178 D. Apiletti et al. Rules shown in Table 2 allow us to infer two quasi tuple constraints, which have a high confidence (e.g., greater than 70%): 1. (Class = Mammalia) → (Reproduction = vivipary) [with confidence of 75.2%] 2. (Class = Aves) → (Reproduction = ovipary) [with confidence of 99.7%] The first rule means that the majority of mammalians (75.2%) stored in the database are vivipary, the second one that almost all the birds (99.7%) are ovipary. To compute the dependency degree between Class and Reproduction we consider all the extracted rules that involve the two attributes (not only those that are reported in Table 2) and we obtain a value of 0.95. Supposing to set a threshold of 0.05, this value is high enough to state that there is a quasi functional dependency between the two attributes, as reported in Table 3. Table 3. Dependency degree between Class and Reproduction Quasi-functional dependency Dependency degree Class → Reproduction 0.95 4.2 Violations of Constraints and Dependencies Constraint extraction has the double aim of discovering hidden relationships among data, which may improve domain knowledge and data quality, and investigating anomalies with respect to these relationships. If a quasi tuple constraint or a quasi functional dependency has been detected, it means that there are few cases in which the relationship is not valid. Such cases are anomalies with respect to the frequent ones. These anomalies can be errors or exceptions in the data. Analyzing such anomalies can be useful in different application domains to perform data cleaning or improve the context knowledge. To better investigate the nature of the detected anomalies, we can analyze the confidence of the rules that involve the attribute values of the quasi tuple constraint or the quasi functional dependency. If this value is very low (compared to the confidence value of the other rules), we can strongly suggest that this is an error, otherwise it is more likely to be a correct exception. With respect to the Animal relation introduced in the previous section, we investigate the rules that involve Class and Reproduction with a low confidence (i.e., lower than 30%). We find the following rules: 1. (Class = Mammalia) → (Reproduction = ovipary) [with confidence of 24.8%] 2. (Class = Aves) → (Reproduction = vivipary) [with confidence of 0.3%] Both of them represent interesting cases. The first one is a correct, albeit infrequent, relationship, since there are some mammalians which lay eggs (such as the Ornithorhynchus). The second one is an error, since there is no bird that is not ovipary. There is a mistake in the 100th row because the Passer Domesticus has an ovipary reproduction.
Extraction of Constraints from Biological Data 179 Errors and anomalies may be distinguished by analyzing the confidence value of the rules. The confidence of the second rule (0.3%) is at least one order of magnitude smaller than the average confidence values of the other rules. Hence, we can conclude that this is probably an error. 5 Experiments on Biological Data It is about thirty years that biological data are generated from a variety of biomedical devices and stored at an increasing rate in public repositories. Thus, data constraints can be unknown and it is difficult to recognize the structure and the relationships among data. In this section we present experiments showing the application of the constraint extraction to biological databases. Two kinds of experiments are reported. The first aims at validating the constraint extraction method by applying the proposed framework to a database whose constraints and dependencies are already known in advance. The second extracts and analyzes quasi constraints to discover semantic anomalies and inconsistencies due to possible violations. 5.1 Biological Databases We selected two databases whose structure and dependencies among data were known in order to verify the accuracy of our method. We considered the SCOP (Structural Classification Of Proteins, version 1.71, http://scop.berkeley.edu) and CATH (Class, Architecture, Topology and Homologous superfamily, version 3.0.0, http://www.cathdb.info) databases, which are characterized by a hierarchical structure, similarly to many biological data sources. However, the method is independent of the hierarchical structure, since the Apriori algorithm and our analysis can be applied to different data models ranging from the relational model to XML. The SCOP database classifies about 30.000 proteins in a hierarchical tree of seven levels. From the root to the leaves they are: class, fold, superfamily, family, protein, species, as shown in Fig. 2. This tree structure is particularly suitable for verifying the tuple constraints and functional dependencies that can be extracted by our tool. In this database tuple constraints are represented by tree edges, while functional dependencies are represented by the tree hierarchy. For example we expect to find the following association rules: • (Superfamily=alpha helical ferredoxin)→(Fold=globin-like) with confidence of 100% • (Superfamily=globin-like)→(Fold=globin-like) with confidence of 100% ∑s i Superfamily→Fold • = 1 In this way we can deduce that (NOT(Superfamily=alpha helical ferredoxin) OR (Fold=globin-like)) and (NOT(Superfamily=globin-like) OR (Fold=globinlike)) are tuple constraints and Superfamily→Fold is a functional dependency.
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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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Substructure Analysis of Metabolic
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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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