Abstract
It is an important subject to extract feature genes from microarray expression profiles in the study of computational biology. Based on an improved genetic algorithm (IGA), a feature selection method is proposed in this paper to find a feature gene subset so that the genes related to diseases could be kept and the redundant genes could be eliminated more effectively. In the proposed method, the information entropy is used as a separate criterion, and the crossover and mutation operators in the genetic algorithm are improved to control the number of the feature genes in the subset. After analyzing the microarray expression data, the artificial neural network (ANN) is used to evaluate the feature gene subsets obtained in different parameter conditions. Simulation results show that the proposed method can be used to find the optimal or quasi-optimal feature gene subset with more useful and less redundant information.
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References
Lee, C.K., Klopp, R.G., Weindruch, R., et al.: Gene expression profile of aging and its retardation by caloric restriction. Science 285(5432), 1390–1393 (1999)
Bo, T.H., Jonassen, I.: New feature subset selection procedures for classification of expression profiles. Genome Biology 3(4), 0017.1–0017.11 (2002)
Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. IEEE Intelligent Systems & Their Applications 13(2), 44–49 (1998)
Li, X., Rao, S.Q., Wang, Y.D., et al.: Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. Nucleic Acids Research 32(9), 2685–2694 (2004)
Mayoral, M.M.: Renyi’s entropy as an index of diversity in simple-stage cluster sampling. Information Sciences 105(3), 101–114 (1998)
Khan, J., Wei, J.S., Ringner, M., et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 7(6), 673–679 (2001)
Alon, U., Barkai, N., Notterman, D.A., et al.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Cell Biology 96(12), 6745–6750 (1999)
Lv, S.L., Wang, Q.H., Li, X., et al.: Two feature gene recognition methods based on decision forest. China Journal of Bioinformatics 3(2), 19–22 (2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhang, C., Liang, Y., Xiong, W., Ge, H. (2006). Selection for Feature Gene Subset in Microarray Expression Profiles Based on an Improved Genetic Algorithm. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_20
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DOI: https://doi.org/10.1007/11941439_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
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