Data integration through protein ontology
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Traditional approaches to integrate protein data generally involved keyword searches, which immediately excludes unannotated or poorly annotated data. An alternative protein annotation approach is to rely on sequence identity, structural similarity, or functional identification. Some proteins have a high degree of sequence identity, structural similarity, or similarity in functions that are unique to members of that family alone. Consequently, this approach can not be generalized to integrate the protein data. Clearly, these traditional approaches have limitations in capturing and integrating data for protein annotation. For these reasons, we have adopted an alternative method that does not rely on keywords or similarity metrics, but instead uses ontology. In this chapter we discuss conceptual framework of protein ontology that has a hierarchical classification of concepts represented as classes, from general to specific; a list of attributes related to each concept, for each class; a set of relations between classes to link concepts in ontology in more complicated ways then implied by the hierarchy, to promote reuse of concepts in the ontology; and a set of algebraic operators for querying protein ontology instances.
This chapter appears in Data Mining with Ontologies: Implementations, Findings, and Frameworks, edited by H. O. Nigro, S. E. G. Cisaro and D. H. Xodo.
Copyright 2006, IGI Global, www.igi-global.com. Posted by permission of the publisher.
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