A Novel Data Mining Technique for Gene Identification in Time-Series Gene Expression Data

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TypeArticle
ConferenceThe 16th European Conference on Artificial Intelligence (ECAI 2004), August 22-27, 2004., Valencia, Spain
Subjectdata mining; genomics; gene identifications; gene expression; time-series; microarray
AbstractThe purpose of this study was to develop a method for identifying useful patterns in gene expression time-series data. We have developed a novel data mining approach that identifies interesting patterns. The method consists of a combination of data pre-processing as well as unsupervised and supervised learning techniques. To evaluate our approach, we have analyzed three time series data sets which investigate the temporal transcriptome changes that occur during: 1) the cell cycle of budding yeast (<em>S. cerevisiae</em>) [3], 2) the epithelial to mesenchymal transition induced by Transforming Growth Factor-?1 in mouse mammary epithelial BRI-JM01 cells, and 3) the program of differentiation induced by retinoic acid in human embryonal teratocarcinoma NT-2 cells. We present the results from all of our experiments, discuss the patterns discovered through the use of our approach and briefly explain future plans and directions for improving our method.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number47142
NPARC number5764970
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Record identifier14d41bd3-87fb-46f6-b86b-291ff0eb7c13
Record created2009-03-29
Record modified2016-05-09
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