Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.945
Gene expression microarray data are highly multidimensional and contain high level of noise. Most of these data involve multiple heterogeneous dynamic patterns depending on disease under study. In addition, possible errors might also be introduced along data collection path if multiple sites and methods are used. In this paper a combined data mining method, i.e., neural network with K-means clustering via principal component analysis (PCA), is proposed to address the data complexity issues when conducting gene expression profile mining. The proposed method was tested on gene expression profile in lung adenocarcinoma, collected from multiple cancer research centers, for survival prediction and risk assessment. The results from the proposed method were analyzed, and further studies for future improvement of the proposed method were also recommended
gene expression, lung cancer, clustering analysis, k-mean, PCA, neural network
T. C. Chen, M. E. Edgerton, S. Sanga, V. Cristini and T. Y. Chou, "Neural Network with K-Means Clustering via PCA for Gene Expression Profile Analysis," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 670-673.