Abstract
T-score between classes and gene expressions is widely used for gene ranking in microarray gene expression data analysis. We propose to use only support vector points for computation of t-scores for gene ranking. The proposed method uses backward elimination of features, similar to Support Vector Machine Recursive Feature Elimination (SVM-RFE) formulation, but achieves better results than SVM-RFE and t-score based feature selection on three benchmark cancer datasets.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Golub, T., Slonim, D., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J., Coller, H., Loh, M., Downing, J., Caligiuri, M., Bloomfield, C., Lander, E.: Molecular classification of cancer: Class discovery and class prediction by gene expression. Science 286, 531–537 (1999)
Inza, I., Larranaga, P., Blanco, R., Cerrolaza, A.: Filter versus wrapper gene selection approaches in DNA microarray domains. Artificial Intelligence Medicine 31, 91–103 (2004)
Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Network 5, 537–550 (1994)
Liu, X., Krishnan, A., Mondry, A.: An entropy-based gene selection method for cancer classification using microarray data. BMC Bioinformatics 6, 76 (2005)
Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinformatics Computational Biology 3, 185–205 (2005)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Analysis Machine Intelligence 27, 1226–1237 (2005)
Ooi, C., Chetty, M., Teng, S.: Differential prioritization between relevance and redundancy in correlation-based feature selection techniques for multiclass gene expression data. BMC Bioinformatics 7, 320–339 (2006)
Zhang, J., Deng, H.: Gene selection for classification of microarray data based on bayes error. BMC Bioinformatics 8, 370 (2007)
Rakotomamonjy, A.: Variable selection using svm criteria. J. Machine Learning Research (Special Issue on Variable Selection) 3, 1357–1370 (2003)
Kai-Bo, D., Rajapakse, J., Wang, H., Azuaje, F.: Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Trans. Nanobioscience 4, 228–234 (2005)
Mundra, P., Rajapakse, J.: SVM-RFE with relevancy and redundancy criteria for gene selection. In: Rajapakse, J., Schmidt, B., Volkert, L.G. (eds.) PRIB 2007. LNCS (LNBI), vol. 4774, pp. 242–252. Springer, Heidelberg (2007)
Guyon, I., Weston, J., Barhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Machine Learning 46, 389–422 (2002)
Rajapakse, J., Kai-Bo, D., Yeo, W.: Proteomic cancer classification with mass spectrometry data. American. J. Pharmacogenomics 5, 281–292 (2005)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2001)
Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., Levine, A.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. PNAS 96, 6745–6750 (1999)
Singh, D., Febbo, P., Ross, K., Jackson, D., Manola, J., Ladd, C., Tamayo, P., Renshaw, A., D’Amico, A., Richie, J., Lander, E., Loda, M., Kantoff, P., Golub, T., Sellers, W.: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203–209 (2002)
Chang, C., Lin, C.: Libsvm: A library for support vector machines (2001), www.csie.ntu.edu.tw/~cjlin/libsvm
Azuaze, F.: Genomic data sampling and its effect on classification performance assessment. BMC Bioinformatics 4, 5 (2003)
Lai, C., Reinders, M., van’t Veer, L., Wessels, L.: A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets. BMC Bioinformatics 7, 235 (2006)
Niijima, S., Kuhara, S.: Recursive gene selection based on maximum margin criterion: a comparison with svm-rfe. BMC Bioinformatics 7, 543 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mundra, P.A., Rajapakse, J.C. (2008). Support Vector Based T-Score for Gene Ranking. In: Chetty, M., Ngom, A., Ahmad, S. (eds) Pattern Recognition in Bioinformatics. PRIB 2008. Lecture Notes in Computer Science(), vol 5265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88436-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-88436-1_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88434-7
Online ISBN: 978-3-540-88436-1
eBook Packages: Computer ScienceComputer Science (R0)