Abstract
This paper proposes a new method for tumor classification using gene expression data. In this method, we first employ independent component analysis (ICA) to model the gene expression data, then apply optimal scoring algorithm to classify them. To show the validity of the proposed method, we apply it to classify two DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that the method is efficient and feasible.
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Zheng, CH., Shang, L., Chen, Y., Huang, ZK. (2006). Penalized Independent Component Discriminant Method for Tumor Classification. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_53
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DOI: https://doi.org/10.1007/11816102_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37277-6
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