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This article is part of the supplement: A Semantic Web for Bioinformatics: Goals, Tools, Systems, Applications

Open Access Research

GraphFind: enhancing graph searching by low support data mining techniques

Alfredo Ferro12*, Rosalba Giugno1, Misael Mongiovì1, Alfredo Pulvirenti1, Dmitry Skripin1 and Dennis Shasha3

Author Affiliations

1 Dipartimento di Matematica e Informatica, Università di Catania, Catania, 95125, Italy

2 Dipartimento di Scienze Biomediche, Università di Catania, Catania, 95125, Italy

3 Courant Institute of Mathematical Sciences, New York University, New York, 10012, USA

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BMC Bioinformatics 2008, 9(Suppl 4):S10  doi:10.1186/1471-2105-9-S4-S10

Published: 25 April 2008

Abstract

Background

Biomedical and chemical databases are large and rapidly growing in size. Graphs naturally model such kinds of data. To fully exploit the wealth of information in these graph databases, a key role is played by systems that search for all exact or approximate occurrences of a query graph. To deal efficiently with graph searching, advanced methods for indexing, representation and matching of graphs have been proposed.

Results

This paper presents GraphFind. The system implements efficient graph searching algorithms together with advanced filtering techniques that allow approximate search. It allows users to select candidate subgraphs rather than entire graphs. It implements an effective data storage based also on low-support data mining.

Conclusions

GraphFind is compared with Frowns, GraphGrep and gIndex. Experiments show that GraphFind outperforms the compared systems on a very large collection of small graphs. The proposed low-support mining technique which applies to any searching system also allows a significant index space reduction.