document: Mining coherent gene clusters from gene-sample-time microarray data Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications . In this paper , we explore a novel type of gene-sample-time microarray data sets , which records the expression levels of various genes under a set of samples during a series of time points . In particular , we propose the mining of coherent gene clusters from such data sets . Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series . The coherent gene clusters may identify the samples corresponding to some phenotypes ( e.g. , diseases ) , and suggest the candidate genes correlated to the phenotypes . We present two efficient algorithms , namely the Sample-Gene Search and the Gene-Sample Search , to mine the complete set of coherent gene clusters . We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets . The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters .
doc_bio_tags: O O O O O O B I O O O O O O B I O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O