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Exploring information from the topology beneath the Gene Ontology terms to improve semantic similarity measures
- Source :
- Gene. 586:148-157
- Publication Year :
- 2016
- Publisher :
- Elsevier BV, 2016.
-
Abstract
- Measuring the similarity between pairs of biological entities is important in molecular biology. The introduction of Gene Ontology (GO) provides us with a promising approach to quantifying the semantic similarity between two genes or gene products. This kind of similarity measure is closely associated with the GO terms annotated to biological entities under consideration and the structure of the GO graph. However, previous works in this field mainly focused on the upper part of the graph, and seldom concerned about the lower part. In this study, we aim to explore information from the lower part of the GO graph for better semantic similarity. We proposed a framework to quantify the similarity measure beneath a term pair, which takes into account both the information two ancestral terms share and the probability that they co-occur with their common descendants. The effectiveness of our approach was evaluated against seven typical measurements on public platform CESSM, protein–protein interaction and gene expression datasets. Experimental results consistently show that the similarity derived from the lower part contributes to better semantic similarity measure. The promising features of our approach are the following: (1) it provides a mirror model to characterize the information two ancestral terms share with respect to their common descendant; (2) it quantifies the probability that two terms co-occur with their common descendant in an efficient way; and (3) our framework can effectively capture the similarity measure beneath two terms, which can serve as an add-on to improve traditional semantic similarity measure between two GO terms. The algorithm was implemented in Matlab and is freely available from http://ejl.org.cn/bio/GOBeneath/ .
- Subjects :
- 0301 basic medicine
0206 medical engineering
02 engineering and technology
Similarity measure
Biology
Bioinformatics
computer.software_genre
03 medical and health sciences
Semantic similarity
Terminology as Topic
Yeasts
Genetics
Humans
Databases, Protein
Gene ontology
business.industry
Gene Expression Profiling
Proteins
General Medicine
Semantics
Gene Ontology
030104 developmental biology
Normalized compression distance
Graph (abstract data type)
Artificial intelligence
business
computer
Algorithms
020602 bioinformatics
Natural language processing
Subjects
Details
- ISSN :
- 03781119
- Volume :
- 586
- Database :
- OpenAIRE
- Journal :
- Gene
- Accession number :
- edsair.doi.dedup.....59f3549645b17e1d02307d237e463c48