Paper Info
Title
Gene Functional Similarity Analysis by Definition-based Semantic Similarity Measurement of GO Terms.
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.
Year
DOI
Venue
2014
10.1007/978-3-319-06483-3_18
ADVANCES IN ARTIFICIAL INTELLIGENCE, CANADIAN AI 2014
Keywords
Field
DocType
Semantic Similarity,Gene Functional Similarity,Gene Ontology,Gene Expression,MEDLINE,Bioinformatics,BioNLP,Computational Biology
Semantic similarity,Similarity analysis,Degree of similarity,Information retrieval,Computer science,Gene ontology,Biomedical text mining,Correlation,Artificial intelligence,Natural language processing,Vocabulary
Conference
Volume
ISSN
Citations 
8436
0302-9743
3
PageRank 
References 
Authors
0.38
12
4
Name
Order
Citations
PageRank
Ahmad Pesaranghader1284.20
Ali Pesaranghader2293.16
Azadeh Rezaei381.49
Danoosh Davoodi430.38