Building a Classifier for Integrated Microarray Datasets through Two-Stage Approach

Youngmi Yoon, Jongchan Lee, Sanghyun Park
2006 Sixth IEEE Symposium on BioInformatics and BioEngineering (BIBE'06)  
Since microarray data acquire tens of thousands of gene expression values simultaneously, they could be very useful in identifying the phenotypes of diseases. However, the results of analyzing several microarray datasets which were independently carried out with the same biological objectives, could turn out to be different. One of the main reasons is attributable to the limited number of samples involved in one microarray experiment. In order to increase the classification accuracy, it is
more » ... able to augment the sample size by integrating and maximizing the use of independently-conducted microarray datasets. In this paper, we propose a two-stage approach which firstly integrates individual microarray datasets to overcome the problem caused by limited number of samples, and identifies informative genes, secondly builds a classifier using only the informative genes. The classifier from large samples by integrating independent microarray datasets achieves high accuracy, sensitivity, and specificity on independent test sample dataset.
doi:10.1109/bibe.2006.253321 dblp:conf/bibe/YoonLP06 fatcat:qqphlnbtcrfpln4xxy5dzrjyly