↓ Skip to main content

PLOS

Semantic Particularity Measure for Functional Characterization of Gene Sets Using Gene Ontology

Overview of attention for article published in PLOS ONE, January 2014
Altmetric Badge

Mentioned by

twitter
6 X users

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
34 Mendeley
citeulike
1 CiteULike
Title
Semantic Particularity Measure for Functional Characterization of Gene Sets Using Gene Ontology
Published in
PLOS ONE, January 2014
DOI 10.1371/journal.pone.0086525
Pubmed ID
Authors

Charles Bettembourg, Christian Diot, Olivier Dameron

Abstract

Genetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 9%
Spain 1 3%
Unknown 30 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 41%
Student > Ph. D. Student 6 18%
Student > Master 4 12%
Student > Bachelor 2 6%
Professor > Associate Professor 2 6%
Other 2 6%
Unknown 4 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 44%
Computer Science 9 26%
Biochemistry, Genetics and Molecular Biology 4 12%
Psychology 1 3%
Chemistry 1 3%
Other 0 0%
Unknown 4 12%