Name
Title | ||
|---|---|---|
MFMS: maximal frequent module set mining from multiple human gene expression data sets |
Abstract | ||
|---|---|---|
Advances in genomic technologies have allowed vast amounts of gene expression data to be collected. Protein functional annotation and biological module discovery that are based on a single gene expression data suffers from spurious coexpression. Recent work have focused on integrating multiple independent gene expression data sets. In this paper, we propose a two-step approach for mining maximally frequent collection of highly connected modules from coexpression graphs. We first mine maximal frequent edge-sets and then extract highly connected subgraphs from the edge-induced subgraphs. Experimental results on the collection of modules mined from 52 Human gene expression data sets show that coexpression links that occur together in a significant number of experiments have a modular topological structure. Moreover, GO enrichment analysis shows that the proposed approach discovers biologically significant frequent collections of modules. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2500863.2500869 | BIOKDD |
Keywords | Field | DocType |
maximal frequent edge-sets,multiple human gene expression,coexpression link,multiple independent gene expression,maximally frequent collection,maximal frequent module set,biologically significant frequent collection,human gene expression data,gene expression data,single gene expression data,coexpression graph,protein structure,cohesion | Data mining,Graph,Data set,Annotation,Gene expression,Modular design,Spurious relationship,Mathematics | Conference |
Citations | PageRank | References |
3 | 0.38 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saeed Salem | 1 | 182 | 17.39 |
| Cagri Ozcaglar | 2 | 3 | 1.40 |