Bio-medical Ontologies Maintenance and Change Management

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172 D. Apiletti et al. Along with the ongoing trend to use the XML format in biological databases, some kinds of constraints for XML data have been explored by recent research papers [10][19], such as key, inclusion, inverse and path constraints. Instead functional dependencies other than key dependencies have been little investigated. In [14] the authors address this issue with a subgraph-based approach which captures new kinds of functional dependencies useful in designing XML documents. Furthermore, they analyze various properties such as expressiveness, semantic behaviour and axiomatizations. Constraints in hierarchically structured data have been discussed in [13]. In particular, they focus on functional and key dependencies and investigate how constraints can be used to check the consistency of data being exchanged among different sources. This leads to the constraint propagation problem, which is addressed in the form of propagating XML keys to relational views by providing two algorithms. The importance of translating constraints in data exchanges is also discussed in [9], whose work proposes a suitable language for this task. The proposed language is able to express a wide variety of database constraints and transformations and its development was motivated by experiences on biomedical databases in informatic support to genome research centers. In [8] the authors introduce the notion of pseudo-constraints, which are predicates having significantly few violations. This is a concept similar to the quasi functional dependencies, but they define pseudo-constraints on the Entity- Relationship model, whereas we use association rules to define quasi functional dependencies. Furthermore, they focus on cyclic pseudo-constraints and propose an algorithm for extracting this kind of cyclic pattern. On the contrary, our notion of dependency is an implication between sets of elements and is not bound to the structure of the data source used to mine the pattern. In [7] quasi functional dependencies have been exploited to detect anomalies in the data. On the contrary, in this work the focus is on the extraction of constraints and the anomaly detection is only a further possible application of our method. 3 Background In this section we provide an overview of the main concepts behind the successful discovery of constraints. We start with a survey of the relational model, which is widely adopted in the database design and whose strongly structured format eases the definition of constraints. Then we focus on a particular kind of constraints: integrity constraints and their subtypes. Eventually we conclude with an introduction to association rules, a well-established data mining technique for inferring unknown correlations from data. 3.1 Relational Model There are several ways to describe a database at a logic level, e.g., hierarchical, relational, or object oriented. Nowadays the relational model is the most widely
Extraction of Constraints from Biological Data 173 adopted representation for databases. Every concept is represented by a relation, i.e. a table. In the biological domain well known examples of relational databases are cuticleDB 1 , RCSB Protein Data Bank 2 , Identitag 3 , AbMiner 4 , PfamRDB 5 , and Reactome 6 . Since older databases often lack a relational structure, many tools to parse and load their data into a relational schema have been developed. Indeed, the relational model is more convenient than other data representations. For example the Bio- Warehouse 7 toolkit enables to translate the flat file representation of some databases, such as SwissProt 8 , NCBI 9 , KEGG 10 and GO 11 , into a relational schema. A relation consists of many tuples (rows), each of which represents an instance of an entity (i.e., a concept), and many columns, which represent the attributes (i.e., properties) of the entity. For example, in a protein database, the protein is an entity and its structure, function and sequence are its attributes. Every row related to a specific protein with all its attribute values is an instance of the protein entity. The table that contains all the proteins with their attributes is a relation. The relational model is characterized by a structured, fixed format, since data values have to be homogeneous and have to meet several constraints. The schema constraints may be known or unknown in advance. In both cases our analysis is helpful for detecting and investigating instance constraints, which may suggest novel information and detect errors. 3.2 Constraints Constraints are assertions on permissible or consistent database states and specify data properties that should be satisfied by valid instances of the database. They are usually defined in the form of expressions that provide a boolean value, indicating whether or not the constraint holds. Let us now consider, as an example, a relational database containing information about students of a university. In particular, we consider only a relation Student (StudentID, Name, Age, City, Country, Mail). Each student is described by some attributes, among which there are a unique identifier (StudentID), his/her name (Name), city (City), country (Country) and mail address (Mail). 1 http://biophysics.biol.uoa.gr/cuticle 2 http://pdbbeta.rcsb.org 3 http://pbil.univ-lyon1.fr/software/identitag 4 http://discover.nci.nih.gov/abminer 5 http://sanger.ac.uk/pub/databases/Pfam 6 http://www.reactome.org 7 http://brg.ai.sri.com/biowarehouse 8 www.ebi.ac.uk/swissprot 9 www.ncbi.nih.gov 10 www.genome.jp/kegg 11 www.geneontology.org
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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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72 L. Stanescu, D. Dan Burdescu, an
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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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