Bio-medical Ontologies Maintenance and Change Management

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182 D. Apiletti et al. Rules 1400 1200 1000 800 600 400 200 0 0,100 0,090 0,080 0,070 0,060 0,050 0,045 0,040 0,035 Tolerance 0,030 0,025 0,020 0,015 0,010 SCOP CATH Fig. 3. Number of quasi tuple constraints found for different values of tolerance 1. querying different related databases (e.g., GO, PDB, Swiss Prot, CATH, SCOP), 2. searching relevant information in literature, 3. comparing the obtained results with the schema constraints of the examined database. If the database constraints are known, errors can be distinguished from exceptions automatically. In particular, if the attribute values of a tuple do not satisfy the constraints, we can conclude that it is an error, otherwise it is a biological exception. For example, in the SCOP database, we can consider the following quasi tuple constraint. (fold= Ntn hydrolase-like) → (superfamily=hypothetical protein MTH1020) [c=0.99771] The extracted anomaly rule with respect to this constraint is: (fold= Ntn hydrolase-like) → (superfamily= N-terminal nucleophile aminohydrolases) [c=0. 00229] Given such anomaly, the three above-described approaches can be applied to distinguish between an error and a biological exception. 1. In the CATH database, the same protein is classified in the same alpha+beta class as in SCOP and it has a 4-layer sandwich architecture (and Glutamine topology) that consists of the 4 layers alpha/beta/beta/alpha that are the same for the N-terminal nucleophile aminohydrolases fold found in the SCOP classification. 0,005 0,001
Extraction of Constraints from Biological Data 183 2. In literature evidences can be found that the crystal structure of MTH1020 protein reveals an Ntn-hydrolase fold [22]. 3. The results can be compared to the SCOP constraints (which are known), and confirm the correctness of this relationship (i.e., it is not an error). Finally, applying the same procedure also to the CATH database, we discovered the anomalies reported in Fig. 3. Among these, we noticed the one for the hypothetical protein MTH1020. Thus, the method applied independently on two different databases reveals the same anomaly. This result confirms the consistency of the proposed method for anomaly discovery. 5.4 Quasi Functional Dependencies For the purpose of identifying functional dependencies, the dependency degree between couples of attributes must be equal to 1 (see Eq. (3)). We verified that the proposed method is sound and complete by means of two experiments, which show that it correctly identifies all and only the functional dependencies contained in the database. In the first experiment we executed the algorithm on the SCOP database with the lowest support value. Table 4 shows the detected functional dependencies. The first two columns contain the attributes which are functionally dependent (their names are explained in the following example). The third column represents the dependency degree obtained by applying Eq. (3) on the original data. Only the couples of attributes with dependency degree of 1 are shown. Since the SCOP database constraints are known, we exploited its structural knowledge to prove the correctness of our method. Our method detects all and only the functional dependencies in the SCOP database. Hence, it is sound (i.e., it only detects correct functional dependencies) and complete (i.e., all known functional dependencies are detected). In the second experiment we performed a simulation of fault injection tests. In this way, we demonstrate that also in presence of errors, our method is sound and complete, because it still detects all and only the non-faulted dependencies. We changed random values at different levels of the hierarchy, by substituting the actual value with one randomly taken from another branch of the tree. This kind of misclassification is rather subtle to identify, since the value is acceptable (i.e., it is valid and it is not misspelled), but it assigns a particular protein to the wrong class for one or more levels of the hierarchy. The fourth column of Table 4 represents the dependency degree after the fault injection simulation, where a random fault has been injected for some levels of the classification hierarchy. All the affected attributes, represented in bold in the first two columns, report dependencies whose value falls below 1. Thus, a quasi functional dependency analysis can be performed to detect all the injected faults.
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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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Mining Clinical, Immunological, and
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Mining Clinical, Immunological, and
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Substructure Analysis of Metabolic
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entry compound relation subtype com
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Running Time 4500 4000 3500 3000 25
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6 Conclusion Substructure Analysis
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Completing the Total Wellbeing Puzz
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296 M. Fernandez, M. Villasana, and
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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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