Abstract
Microarrays are capable of detecting the expression levels of thousands of genes simultaneously. In this paper, a new method for gene selection based on independent variable group analysis is proposed. In this method, we first used t-statistics method to select a part of genes from the original data. Then we selected the key genes from the selected genes by t-statistics for tumor classification using IVGA. Finally, we used SVM to classify tumors based on the key genes selected using IVGA. To validate the efficiency, the proposed method is applied to classify three different DNA microarray data sets. The prediction results show that our method is efficient and feasible.
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Zheng, C., Zhang, L., Li, B., Xu, M. (2008). Gene Expression Data Classification Using Independent Variable Group Analysis. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_28
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DOI: https://doi.org/10.1007/978-3-540-87734-9_28
Publisher Name: Springer, Berlin, Heidelberg
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