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Event models for tumor classification with SAGE gene expression data

Event models for tumor classification with SAGE gene expression data

Jin, Xin, Xu, Anbang, Zhao, Guoxing, Ma, Jixin and Bie, Rongfang (2006) Event models for tumor classification with SAGE gene expression data. Lecture Notes in Computer Science, 3992. pp. 775-782. ISSN 0302-9743 (doi:10.1007/11758525_104)

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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.

Item Type: Article
Additional Information: Presented at 6th International Conference on Computational Science (ICCS 2006). Reading, England, May 28-31, 2006.
Uncontrolled Keywords: methodology, event models
Subjects: Q Science > QA Mathematics
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Computer & Computational Science Research Group
School of Computing & Mathematical Sciences > Department of Computer Science
Related URLs:
Last Modified: 14 Oct 2016 09:02
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/1028

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