Name
Abstract | ||
|---|---|---|
Mining multiple gene coexpressions networks allows for identifying context-specific modules, and improving biological function prediction. Frequent subnetworks represent essential biological modules. Existing algorithms for frequent subgraph mining do not scale for large networks. In this work, we propose a greedy approach for mining approximate frequent subgraphs. Experiments on two real coexpression networks demonstrate the effectiveness of the proposed algorithm. Biological enrichment analysis of the reported patterns show that the patterns are biologically relevant and enriched with known biological processes and KEGG pathways. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIBM.2017.8217922 | 2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | DocType | ISSN |
coexpression, graphs, frequent, subgraphs | Conference | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eihab El Radie | 1 | 0 | 1.01 |
| Saeed Salem | 2 | 182 | 17.39 |