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Competitive and self-contained gene set analysis methods applied for class prediction

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1 Author(s)
Henryk Maciejewski ; Institute of Computer Engineering, Control and Robotics, Wroclaw University of Technology, ul. Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland

This paper compares two methodologically different approaches to gene set analysis applied for selection of features for sample classification based on microarray studies. We analyze competitive and self-contained methods in terms of predictive performance of features generated from most differentially expressed gene sets (pathways) identified with these approaches. We also observe stability of features returned. We use the features to train several classifiers (e.g., SVM, random forest, nearest shrunken centroids, etc.) We generally observe smaller classification errors and better stability of features produced by the self-contained algorithm. This comparative study is based on the leukemia data set published in [3].

Published in:

Computer Science and Information Systems (FedCSIS), 2011 Federated Conference on

Date of Conference:

18-21 Sept. 2011