Bio-medical Ontologies Maintenance and Change Management

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58 A.S. Sidhu and M. Bellgard consists of a unique identifier and one or more strings (referred to as terms) that provide a controlled vocabulary for unambiguous and consistent naming. Concepts exist in a hierarchy of IsA and PartOf relations in a directed acyclic graph (DAG) that locates all concepts in the knowledge model with respect to their relationships with other concepts. Eight years have now passed and GO has grown enormously. GO is now clearly defined and is a model for numerous other biological ontology projects that aim similarly to achieve structured, standardized vocabularies for describing biological systems. GO is a structured network consisting of defined terms and the relationships between them that describe attributes of gene products. There are many measures demonstrating its success. The characteristics of GO that we believe are most responsible for its success include: community involvement, clear goals, limited scope, simple, intuitive structure, continuous evolution, active curation, and early use. At present, there are close to 300 articles in PubMed referencing GO. Among large institutional databanks, Swiss-Prot now uses GO for annotating the peptide sequences it maintains. The number of organism groups participating in the GO consortium has grown every quarter-year from the initial three to roughly two dozen. Every conference has talks and posters either referencing or utilizing GO, and within the genome community it has become the accepted standard for functional annotation. 2.2 MGED Ontology The MGED Ontology (MO) was developed by the Microarray Gene Expression Data (MGED) Society. MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to preparation of the biological sample and protocols used to hybridise the RNA and analyse the data (Whetzel et al., 2006). MO is a species-neutral ontology that focuses on commonalities among experiments rather than differences between them. MO is primarily an ontology used to annotate microarray experiments; however, it contains concepts that are universal to other types of functional genomics experiments. The major component of the ontology involves biological descriptors relating to samples or their processing; it is not an ontology of molecular, cellular, or organism biology, such as the Gene Ontology. MO version 1.2 contains 229 classes, 110 properties and 658 instances. 2.3 Open Issues in Biomedical Ontologies Research into different biological systems uses different organisms chosen specifically because they are amenable to advancing these investigations. For example, the rat is a good model for the study of human heart disease, and the fly is a good model for the study of cellular differentiation. For each of these model systems, there is a database employing curators who collect and store the body of biological knowledge for that organism. Mining of Scientific Text and Literature is done to generate a list of keywords that are used as GO terms. However, querying heterogeneous, independent databases in order to draw these inferences
Protein Data Integration Problem 59 is difficult: The different database projects may use different terms to refer to the same concept and the same terms to refer to different concepts. Furthermore, these terms are typically not formally linked with each other in any way. GO seeks to reveal these underlying biological functionalities by providing a structured controlled vocabulary that can be used to describe gene products, and is shared between biological databases. This facilitates querying for gene products that share biologically meaningful attributes, whether from separate databases or within the same database. Association between ontology nodes and proteins, namely, protein annotation through gene ontology, is an integral application of GO. To efficiently annotate proteins, the GO Consortium developed a software platform, the GO Engine, which combines rigorous sequence homology comparison with text information analysis. During evolution, many new genes arose through mutation, duplication, and recombination of the ancestral genes. When one species evolved into another, the majority of orthologs retained very high levels of homology. The high sequence similarity between orthologs forms one of the foundations of the GO Engine. Text information related to individual genes or proteins is immersed in the vast ocean of biomedical literature. Manual review of the literature to annotate proteins presents a daunting task. Several recent papers described the development of various methods for the automatic extraction of text information (Jenssen et al., 2001, Li et al., 2000). However, the direct applications of these approaches in GO annotation have been minimal. A simple correlation of text information with specific GO nodes in the training data predicts GO association for unannotated proteins. The GO Engine combines homology information, a unique protein-clustering procedure, and text information analysis to create the best possible annotations. Recently, Protein Data Bank (PDB) also released versions of the PDB Exchange Dictionary and the PDB archival files in XML format collectively named PDBML (Westbrook et al., 2005). The representation of PDB data in XML builds from content of PDB Exchange Dictionary, both for assignment of data item names and defining data organization. PDB Exchange and XML Representations use the same logical data organization. A side effect of maintaining a logical correspondence with PDB Exchange representation is that PDBML lacks the hierarchical structure characteristic of XML data. A directed acyclic graph (DAG) based ontology induction tool (Mani et al., 2004) is also used to constructs a protein ontology including protein names found in MEDLINE abstracts and in UNIPROT. It is a typical example of text mining the literature and the data sources. It can’t be classified as protein ontology as it represents only the relationship between protein literatures and does not formalize knowledge about the protein synthesis process. Recently, the Genomes to Life Initiative was introduced (Frazier et al., 2003a, Frazier et al., 2003b) close to completion of Human Genome Project (HGP) which finished in April 2003 (Collins et al., 2003). Lessons learnt from HGP will guide ongoing management and coordination of GTL. GTL states an objective: “To correlate information about multiprotein machines with data in major protein databases to better understand sequence, structure and function of protein machines.” This objective can be achieved to some extent only by creating a Generic Protein
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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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Page 19 and 20: Towards Bioinformatics Resourceomes
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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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Classifying Patterns in Bioinformat
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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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