NetMatch: a Cytoscape plugin for searching biological networks.

Abstract Summary: NetMatch is a Cytoscape plugin which allows searching biological networks for subcomponents matching a given query. Queries may be approximate in the sense that certain parts of the subgraph-query may be left unspecified. To make the query creation process easy, a drawing tool is provided. Cytoscape is a bioinformatics software platform for the visualization and analysis of biological networks. Availability: The full package, a tutorial and associated examples are available at the following web sites: http://alpha.dmi.unict.it/~ctnyu/netmatch.html, http://baderlab.org/Software/NetMatch Contact: [email protected]

GraphFind: enhancing graph searching by low support data mining techniques

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

GraphFind: enhancing graph searching by low support data mining techniques-4

on GraphFind and gIndex using real and synthetic databases of graphs. Size indicates the dimension of fingerprint before applying Min-Hashing algorithm. G = number of graphs; L = number of different node labels per graph; N = number of nodes per graph. A description of the synthetic graphs used here is given in [].<p><b>Copyright information:</b></p><p>Taken from "GraphFind: enhancing graph searching by low support data mining techniques"</p><p>http://www.biomedcentral.com/1471-2105/9/S4/S10</p><p>BMC Bioinformatics 2008;9(Suppl 4):S10-S10.</p><p>Published online 25 Apr 2008</p><p>PMCID:PMC2367637.</p><p></p

(Step 1): Store each graph in the database in a set of Berkeley DB tables each corresponding to a label-path-set; that is the set of the id-paths (e

G. (3,0), (3,2) in ) of all the paths representing a label sequence (e.g. CB in ). For each graph only some label-path-sets are shown. The maximum length (number of edges) of a label-path is =3. The fingerprint (index) of a database is a Berkeley DB hash table where each entry in a column is the number of occurrences of a label-path in that graph. Construct the query fingerprint (=3)(Step 2). Compare the fingerprint of the query with the database fingerprint (Step 3): a database graph, for which at least one value in its fingerprint is less than the corresponding value in the fingerprint of the query, is filtered out (Step 4). and are not selected as candidates since they do not contain the path ABCA. Decompose the query into patterns (Step 5) (=3). From each candidate graph, select the label-path-sets corresponding to the patterns in the query (Step 6) and combine the id-paths of such tables following the query decomposition criteria. In the patterns (B, A*BA*), only labels with equal marks (e.g. _, *) represent the same node occurrences. For example, (1,0,3,1) can not be combined with (3,0) because the nodes labeled B must be different (same motivation applies to (1,2,3,1) and (3,2)). The subgraph obtained by combining (1,2,3,1) and (3,0) is shown in “Filtered Database (2)”. They are the only subgraphs that may match the query. Subgraph matching will be performed by applying the VF2 algorithm [] to those subgraphs instead of to the entire graphs.<p><b>Copyright information:</b></p><p>Taken from "GraphFind: enhancing graph searching by low support data mining techniques"</p><p>http://www.biomedcentral.com/1471-2105/9/S4/S10</p><p>BMC Bioinformatics 2008;9(Suppl 4):S10-S10.</p><p>Published online 25 Apr 2008</p><p>PMCID:PMC2367637.</p><p></p

Preprocessing and Querying performances of GraphFind, GraphGrep and GFrowns on a single graph database

The graph is a Irregular 3D with 10000 nodes and 5 labels []. Index Size refers to the fingerprint database matrix and the graph representation. N is the number of nodes in the query. The query time is the average obtained by 10 different runs. gIndex is not reported since it does not treat graphs with thousands of nodes.<p><b>Copyright information:</b></p><p>Taken from "GraphFind: enhancing graph searching by low support data mining techniques"</p><p>http://www.biomedcentral.com/1471-2105/9/S4/S10</p><p>BMC Bioinformatics 2008;9(Suppl 4):S10-S10.</p><p>Published online 25 Apr 2008</p><p>PMCID:PMC2367637.</p><p></p