Event models for tumor classification with SAGE gene expression data

Authors:
Xin Jin;Anbang Xu;Guoxing Zhao;Jixin Ma;Rongfang Bie
Affiliations:
College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;School of Mathematical Sciences, Beijing Normal University, Beijing, P.R. China;School of Computing and Mathematical Science, University of Greenwich, London, U.K.;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Year:
2006

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Abstract

Serial Analysis of Gene Expression (SAGE) is a relatively new method for monitoring gene expression levels and is expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. A promising application of SAGE gene expression data is classification of tumors. In this paper, we build three event models (the multivariate Bernoulli model, the multinomial model and the normalized multinomial model) for SAGE data classification. Both binary classification and multicategory classification are investigated. Experiments on two SAGE datasets show that the multivariate Bernoulli model performs well with small feature sizes, but the multinomial performs better at large feature sizes, while the normalized multinomial performs well with medium feature sizes. The multinomial achieves the highest overall accuracy.