Abstract #300456

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JSM 2003 Abstract #300456
Activity Number: 283
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #300456
Title: Comparing Classification Methods for Microarray Data
Author(s): Michael Man*+ and Kjell D. Johnson and Greg Dyson
Companies: Pfizer, Inc. and Pfizer, Inc. and University of Michigan
Address: Biometrics, Ann Arbor, MI, 48105-2430,
Keywords: genomics ; microarray ; classification ; bootstrap
Abstract:

Many applications of microarray technologies entail building predictive models. Since it is important to build an accurate model, one needs to perform a comparison of classification methods that are used to generate models. We compared several classic and state-of-the-art classification methods using two standard data sets from recent literature. Our analyses showed that partial least squares and support vector machines give the most accurate results. By using bootstrap crossvalidation, we can identify the frequently misclassified samples. In addition, by using multiple classification methods, we can gain more confidence in prediction or identification of mislabeled sample(s).


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