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

By Mihail Popescu, Dong Xu

An ontology is a suite of vocabulary phrases with explicitly said meanings and family with different phrases. almost immediately, more and more ontologies are being outfitted and used for annotating info in biomedical learn. because of the large volume of information being generated, ontologies are actually getting used in several methods, together with connecting diversified databases, refining seek services, analyzing experimental/clinical information, and inferring wisdom. This state of the art source introduces researchers to newest advancements in bio-ontologies. The e-book offers the theoretical foundations and examples of ontologies, in addition to functions of ontologies in biomedicine, from molecular degrees to medical degrees. Readers additionally locate information on technological infrastructure for bio-ontologies. This entire, one-stop quantity provides quite a lot of sensible bio-ontology details, providing pros designated suggestions within the clustering of organic info, protein type, gene and pathway prediction, and textual content mining.

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Extra info for Data Mining in Biomedicine Using Ontologies (Artech House Series Bioinformatics & Biomedical Imaging)

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10). 3 Concept hierarchy. 21) 36 Ontological Similarity Measures The relationship to Lin’s IC semantic similarity measure may be established by modifying weights for the is a links from a constant 1 to a weight indicating the strength between the parent and child concepts. The weight of the is a links is the difference between the information content of the child node c and the information content of the parent node parent(c); that is, w = IC(c) − IC(parent(c)). This difference indicates how much information is gained by moving from the parent to the child.

For example, the distance between plant and animal is 2 in WordNet, since their common parent, is living thing. The distance between zebra and horse is also 2, since their common parent is equine. Intuitively, one would judge zebra and horse to be more closely related than plant and animal. Solely counting links between nodes is not sufficient. To overcome the limitation of simple edge counting, the edges were weighted to reflect the difference in edge distances. Earlier approaches [25, 29], hand-weighted each edge.

Each type of relation r has a weight range between its own minr and maxr. The actual value in that range for r depends on nr (X), the number of relations of type r leaving node X. This value, referred to as the type specific fanout (TSF) factor, incorporates the dilution of the strength of connotation between a source and target node as a function of the number of like relations that the source node has. This factor reflects that asymmetry might exist between the two nodes so that the strength of connotation in one direction differs from that in the other direction.

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