Extended UTMGO

An Extended UTMGO is a Hybrid Semantic Similarity Measure that is based on information content (node-based), semantic distance (edge-based) as well as measuring the depth of the semantic network.

• AKA: Othman-Deris-Illias Semantic Similarity Measure.
• Context:
  • It was first developed by (Othman et al., 2008).
  • It is an extension to the Basic UTMGO Browser.
• Example(s):
  • …
• Counter-Example(s):
  • Dong-Hussain-Chang Hybrid Concept Similarity Measure,
  • G-SESAME,
  • an Edge-based Semantic Similarity Measure,
  • a Node-based Semantic Similarity Measure,
  • a Node-and-Relation-Content-based Semantic Similarity Measure.
  • a Pairwise Semantic Similarity Measure,
  • a Group wise Semantic Similarity Measure.
• See: Semantic Similarity Measure, Semantic Similarity Neural Network, Semantic Word Similarity Measure, Gene Semantic Similarity Measure, Semantic Relatedness Measure, Similarity Matrix, Basic Local Alignment Search Tool (BLAST), Gene Ontology.

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References

2008

• (Othman et al., 2008) ⇒ Razib M.Othmana, Safaai Deris, and Rosli M.Illias (2008) "A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences" In: Journal of Biomedical Informatics 41(1), Elsevier.
  • QUOTE: A GO-based tool named extended UTMGO is developed to demonstrate the method. The extended UTMGO employs a GO browser named basic UTMGO for implementing the second component. The JAligner engine (http://jaligner.sourceforge.net) that uses the Smith-Waterman algorithm has been integrated and modified to perform the sequence alignment and to comply with the extended UTMGO.

    (...)

    The semantic similarity measure algorithm is added into the parallel genetic algorithm to measure the similitude strength between the GO terms during the creation of initial population and calculation of fitness value. The semantic similarity measure algorithm used is a combination of information content (node-based) and conceptual distance (edge-based). The information content is used to get the amount of information the GO terms share in common, whereas the conceptual distance is applied to know the depth and the local network density of the GO terms.

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