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- W131436534 abstract "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 3dimensional 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 e‐cient 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 e‐cient and efiective to flnd meaningful coherent gene clusters." @default.
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- W131436534 date "2004-01-01" @default.
- W131436534 modified "2023-09-24" @default.
- W131436534 title "Mining Coherent Gene Clusters from Three-Dimensional Microarray Data ⁄" @default.
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