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A data mining method to predict transcriptional regulatory sites based on differentially expressed genes in human genome

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6 Author(s)
Hsien-Da Huang ; Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ., Chung-li, Taiwan ; Huei-Lin Chang ; Tsung-Shan Tsou ; Baw-Jhiune Liu
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Very large-scale gene expression analysis, i.e., UniGene and dbEST, are provided to find those genes with significantly differential expression in specific tissues. The differentially expressed genes in a specific tissue are potentially regulated concurrently by a combination of transcription factors. This study attempts to mine putative binding sites on how combinations of the known regulatory sites homologs and over-represented repetitive elements are distributed in the promoter regions of considered groups of differentially expressed genes. We propose a data mining approach to statistically discover the significantly tissue-specific combinations of known site homologs and over-represented repetitive sequences, which are distributed in the promoter regions of differential gene groups. The association rules mined would facilitate to predict putative regulatory elements and identify genes potentially co-regulated by the putative regulatory elements.

Published in:

Bioinformatics and Bioengineering, 2003. Proceedings. Third IEEE Symposium on

Date of Conference:

10-12 March 2003