JISE


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Journal of Information Science and Engineering, Vol. 19 No. 6, pp. 923-942


A Data Mining Method to Predict Transcriptional Regulatory Sites Based on Differentially Expressed Genes in Human Genome


Hsien-Da Huang1, Huei-Lin Chang4, Tsung-Shan Tsou3
Baw-Jhiune Liu4 and Jorng-Tzong Horng1,2,*

1Department of Computer Science and Information Engineering 
2Department of Life Science 
3Institute of Statistics 
National Central University 
Chungli, 320 Taiwan 
4Department of Computer Science and Engineering 
Yuan-Ze University 
Chungli, 320 Taiwan 
*E-mail: horng@db.csie.ncu.edu.tw


    Very large-scale gene expression analysis, i.e., UniGene and dbEST, is 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 differentially gene groups. The association rules mined would facilitate to predict putative regulatory elements and identify genes potentially co-regulated by the putative regulatory elements.


Keywords: regulatory site, transcription factor, data mining, gene expression, UniGene, EST

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