Mining differential top-k co-expression patterns from time course comparative gene expression datasets.

ABSTRACT:

Background

Frequent pattern mining analysis applied on microarray dataset appears to be a promising strategy for identifying relationships between gene expression levels. Unfortunately, too many itemsets (co-expressed genes) are identified by this analysis method since it does not consider the importance of each gene within biological processes to a cellular response and does not take into account temporal properties under biological treatment-control matched conditions in a microarray dataset.

Results

We propose a method termed TIIM (Top-k Impactful Itemsets Miner), which only requires specifying a user-defined number k to explore the top k itemsets with the most significantly differentially co-expressed genes between 2 conditions in a time course. To give genes different w

SUBMITTER: Liu YC 

PROVIDER: S-EPMC3751367 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature