Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data.

ABSTRACT:

Background

The association-rules discovery (ARD) technique has yet to be applied to gene-expression data analysis. Even in the absence of previous biological knowledge, it should identify sets of genes whose expression is correlated. The first association-rule miners appeared six years ago and proved efficient at dealing with sparse and weakly correlated data. A huge international research effort has led to new algorithms for tackling difficult contexts and these are particularly suited to analysis of large gene-expression matrices. To validate the ARD technique we have applied it to freely available human serial analysis of gene expression (SAGE) data.

Results

The approach described here enables us to designate sets of strong association rules. We normalized the SAGE data b

SUBMITTER: Becquet C 

PROVIDER: S-EPMC151169 | biostudies-literature | 2002

REPOSITORIES: biostudies-literature