Data Mining in Biomedicine Using Ontologies (Artech House by Mihail Popescu, Dong Xu

By Mihail Popescu, Dong Xu

An ontology is a collection of vocabulary phrases with explicitly said meanings and kin with different phrases. almost immediately, more and more ontologies are being equipped and used for annotating facts in biomedical learn. because of the large volume of knowledge being generated, ontologies are actually getting used in different methods, together with connecting diverse databases, refining seek services, reading experimental/clinical information, and inferring wisdom. This state of the art source introduces researchers to most up-to-date advancements in bio-ontologies. The ebook presents the theoretical foundations and examples of ontologies, in addition to purposes of ontologies in biomedicine, from molecular degrees to medical degrees. Readers additionally locate info on technological infrastructure for bio-ontologies. This finished, one-stop quantity provides a variety of useful bio-ontology info, providing execs specific counsel within the clustering of organic information, protein category, gene and pathway prediction, and textual content mining.

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Cimino, J. , and X. Zhu, “The Practical Impact of Ontologies on Biomedical Informatics,” Methods Inf Med, Vol. 45, Supplement 1, 2006, pp. 124–135. , “The GOA Database in 2009—An Integrated Gene Ontology Annotation Resource,” Nucl. , Vol. 37, Supplement 1, 2009, pp. D396–D403. , “The Compositional Structure of Gene Ontology Terms,” Pac Symp Biocomput, 2004, p. 214–225. Mungall, C. , “Obol: Integrating Language and Meaning in Bio-Ontologies,” Comparative and Functional Genomics, Vol. 5, Nos. 6–7, 2004, pp.

This similarity measure, 30 Ontological Similarity Measures however, is not actually an ontological similarity measure in the traditional sense, since it does not assess similarity between concepts in an ontology. Instead, it determines overall similarity between objects described by ontological concepts. One version of the fuzzy-measure-based ontological similarity, SAFMS, augments the sets X and Y with the lowest or nearest common ancestor of every pair (xi, yj). For more details on the use of the fuzzy-measure-based approach to assessing similarity between gene products, see [60].

This is the principle success of the GO, but the use of ontologies as drivers for integration at either the level of schema or the level of the values in the schema are long-standing within bioinformatics and computer science [10]. 6) were early examples. Once data is described, it can be queried and analyzed in terms of its biological meaning, providing new aspects for looking into the data. As biology is often portrayed as a descriptive science, the role of ontologies in bioinformatics will undoubtedly continue.

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