| Download | - View accepted manuscript: A Novel Data Mining Technique for Gene Identification in Time-Series Gene Expression Data (PDF, 739 KiB)
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| Author | Search for: Famili, Fazel; Search for: Liu, Ziying; Search for: Ouyang, Junjun; Search for: Walker, P.R.; Search for: Smith, B.; Search for: O'Connor, M.; Search for: Lenferink, A. |
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| Format | Text, Article |
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| Conference | The 16th European Conference on Artificial Intelligence (ECAI 2004), August 22-27, 2004, Valencia, Spain |
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| Subject | data mining; genomics; gene identifications; gene expression; time-series; microarray |
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| Abstract | The 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. |
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| Publication date | 2004 |
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| In | |
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| Language | English |
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| NRC number | NRCC 47142 |
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| NPARC number | 5764970 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 14d41bd3-87fb-46f6-b86b-291ff0eb7c13 |
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| Record created | 2009-03-29 |
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| Record modified | 2021-01-05 |
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