Abstract
Feature subset selection for outcome prediction is a critical issue in large scale microarray experiments in cancer research. This paper introduces an integrative approach that combines significant gene expression analysis, the genetic algorithm and machine learning for selecting informative gene markers and for predicting tumor outcomes including survival outcomes. In case of survival data, full use of individual’s survival information (both censored and uncensored) is made in selecting informative genes for survival outcome prediction. Applications of our method to published microarray data on epithelial ovarian cancer survival and breast cancer metastasis have identified prognostic genes that predict individual survival and metastatic outcomes with improved power while basing on considerably shorter gene lists.
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References
Baker, S.G., Kramer, B.S.: Identifying genes that contribute most to good classification in microarrays. BMC Bioinformatics 7, 407 (2006)
Brown, M.P., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T.S., Ares, M., Haussler, D.: Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. USA. 97, 262–267 (2000)
Chen, J.J.: Key aspects of analyzing microarray gene-expression data. Pharmacogenomics 8, 473–482 (2007)
Ein-Dor, L., Kela, I., Getz, G., Givol, D., Domany, E.: Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21, 171–178 (2005)
Li, L., Jiang, W., Li, X., Moser, K.L., Guo, Z., Du, L., Wang, Q., Topol, E.J., Wang, Q., Rao, S.: A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. Genomics 85, 16–23 (2005)
Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23, 2507–2517 (2007)
Spentzos, D., Levine, D.A., Ramoni, M.F., Joseph, M., Gu, X., Boyd, J., Libermann, T.A., Cannistra, S.A.: Gene expression signature with independent prognostic significance in epithelial ovarian cancer. J. Clin. Oncol. 22, 4700–4710 (2004)
Stefanini, F.M., Camussi, A.: The reduction of large molecular profiles to informatic components using a genetic algorithm. Bioinformatics 16, 923–931 (2000)
Thomassen, M., Tan, Q., Eiriksdottir, F., Bak, M., Cold, S., Kruse, T.A.: Prediction of metastasis from low-malignant breast cancer by gene expression profiling. International Journal of Cancer 120, 1070–1075 (2006)
van t̀ Veer, L.J., Dai, H., van de Vijver, M.J., et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002)
Wei, H., Billings, S.A.: Feature subset selection and ranking for data dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 162–166 (2007)
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Tan, Q., Thomassen, M., Jochumsen, K.M., Zhao, J.H., Christensen, K., Kruse, T.A. (2008). Evolutionary Algorithm for Feature Subset Selection in Predicting Tumor Outcomes Using Microarray Data. In: Măndoiu, I., Sunderraman, R., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2008. Lecture Notes in Computer Science(), vol 4983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79450-9_39
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DOI: https://doi.org/10.1007/978-3-540-79450-9_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79449-3
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