Publication
Title
Mining the enriched subgraphs for specific vertices in a biological graph
Author
Abstract
In this paper, we present a subgroup discovery method to find subgraphs in a graph that are associated with a given set of vertices. The association between a subgraph pattern and a set of vertices is defined by its significant enrichment based on a Bonferroni-corrected hypergeometric probability value. This interestingness measure requires a dedicated pruning procedure to limit the number of subgraph matches that must be calculated. The presented mining algorithm to find associated subgraph patterns in large graphs is therefore designed to efficiently traverse the search space. We demonstrate the operation of this method by applying it on three biological graph data sets and show that we can find associated subgraphs for a biologically relevant set of vertices and that the found subgraphs themselves are biologically interesting.
Language
English
Source (journal)
IEEE/ACM transactions on computational biology and bioinformatics / Institute of Electrical and Electronics Engineers [New York, N.Y.] - New York, N.Y.
Publication
New York, N.Y. : 2016
ISSN
1545-5963
Volume/pages
(2016), p. 1-12
Full text (Publishers DOI)
Full text (open access)
UAntwerpen
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Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
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Record
Identification
Creation 21.06.2016
Last edited 22.06.2016
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