Citation

Pesaranghader, Ahmad and Pesaranghader, Ali and Rezaei, Azadeh and Davoodi, Danoosh (2014) Gene functional similarity analysis by definition-based semantic similarity measurement of GO terms. In: Advances in Artificial Intelligence. Lecture Notes in Computer Science (8436). Springer International Publishing, pp. 203-214. ISBN 978-3-319-06482-6

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Abstract

The rapid growth of biomedical data annotated by Gene Ontology (GO) vocabulary demands an intelligent method of semantic similarity measurement between GO terms remarkably facilitating analysis of genes functional similarities. This paper introduces two efficient methods for measuring the semantic similarity and relatedness of GO terms. Generally, these methods by taking definitions of GO terms into consideration, address the limitations in the existing GO term similarity measurement methods. The two developed and implemented measures are, in essence, optimized and adapted versions of Gloss Vector semantic relatedness measure for semantic similarity/relatedness estimation between GO terms. After constructing optimized and similarity-adapted definition vectors (Gloss Vectors) of all the terms included in GO, the cosine of the angle between terms’ definition vectors represent the degree of similarity or relatedness for two terms. Experimental studies show that this semantic definition-based approach outperforms all existing methods in terms of the correlation with gene expression data.

Item Type:	Book Section
Additional Information:	Book Subtitle: Proceedings 27th Canadian Conference on Artificial Intelligence, Canadian AI 2014, Montréal, QC, Canada, May 6-9, 2014.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	16 Jul 2014 03:14
Last Modified:	16 Jul 2014 03:14
URII:	http://shdl.mmu.edu.my/id/eprint/5620

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