An Approach to Subtype Recognition and Extraction of Informative Genes for SRBCT

Ai Xiang He; Yong Qiang Wang; Yan Rong Shi

doi:10.4028/www.scientific.net/AMM.40-41.619

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 40-41An Approach to Subtype Recognition and Extraction...

An Approach to Subtype Recognition and Extraction of Informative Genes for SRBCT

Abstract:

An approach to tumor molecular classification based on their gene expression profiles was presented. A new measure known as Between-groups to within-groups sums of squares ratio (BSS/WSS) was used as the criterion of screening predictive genes for SRBCT subtype recognition. The 152 genes were chosen by this criterion and formed the feature set whose subsets would be used to create MSVM models to identify the subtypes. The trained MSVM based on the top 25 genes ranked by BSS/WSS was able to achieve 100% accuracy on the training and blind test dataset. Then this subset was analyzed by the dissimilarity distance to remove its redundancy. As a result, the 15 genes were retained with the same accuracy as the subset of 25 genes and were regarded as the final subset. Comparison with other methods demonstrates efficiency and feasibility of the method and the predictive models proposed in this work.

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Periodical:

Applied Mechanics and Materials (Volumes 40-41)

Pages:

619-624

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.40-41.619

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Online since:

November 2010

Authors:

Ai Xiang He, Yong Qiang Wang, Yan Rong Shi

Keywords:

Gene Expression Profile, Multi-Category Support Vector Machine, SRBCT

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References

[1] S. Ramaswamy, T. R. Golub. DNA Microarrays in Clinical Oncology. Journal of Clinical Oncology. 2002, 20(7): 1932~(1941).

DOI: 10.1200/jco.2002.20.7.1932

Google Scholar

[2] J. Khan, J. S. Wei, M. Ringner, et al. Classification and Diagnostic Prediction of Cancers using Gene Expression Profiling and Artificial Neural Networks. Nature Med. 2001, 7(6): 673~679.

DOI: 10.1038/89044

Google Scholar

[3] R. Tibshirani, T. Hastie, B. Narasimhan, et al. Diagnosis of Multiple Cancer Types by Shrunken Centroids of Gene Expression. PNAS. 2002, 99: 6567~6572.

DOI: 10.1073/pnas.082099299

Google Scholar

[4] G. Yeo, T. Poggio. Multiclass Classification of SRBCT. CBCL Meno. 2001, 018: 83~84.

Google Scholar

[5] V. N. Vapnik. Statistical Learning Theroy. NewYork: Wiley Interscience, (1998).

Google Scholar

[6] Y. Lee, Y. Lin and G. Wahba. Multicategory Support Vector Machines, Theory, and Application to the Classification of Microarray Data and Satellite Radiance Data. Journal of the American Statistical Association. 2004, 99: 67~68.

DOI: 10.1198/016214504000000098

Google Scholar