Feature dimension reduction for microarray data analysis using Locally Linear Embedding

Shi Chao, Chen Lihui, in Proceedings of the 3^rd Asia-Pacific Bioinformatics Conference, Advances in Bioinformatics and Computational Biology, vol. 1, 2005. download!

Abstract

Cancer classification is one major application of microarray data analysis. Due to the ultra high dimensionality nature of microarray data, data dimension reduction has drawn special attention for such type of data analysis. The currently available data dimension reduction methods are either supervised, where data need to be labeled, or computational complex. In this paper, we proposed to use a revised locally linear embedding(LLE) method, which is purely unsupervised and fast as the feature extraction strategy for microarray data analysis. Three public available microarray datasets have been used to test the proposed method. The effectiveness of LLE is evaluated by the classification accuracy of a SVM classifier. Generally, the results are promising.

High dimensional gene expression data dimension reduction

Shi Chao, Chen Lihui, in Proceedings of IEEE Conference on Cybernetics and Intelligent Systems (CIS), Dec 2004. download!

Abstract

Gene expression data analysis is a new approach in cancer diagnosis. Feature selection is an important preprocessing step in gene expression data clustering. In this paper, we demonstrate the effectiveness of feature grouping approach in feature dimension reduction. In our proposed framework, large number of features is grouped to form several feature subsets. By criteria of clustering accuracy, one feature subset is chosen as the candidate subset for further processing by PCA or entropy ranking, and the final feature subset are formed by selecting the features from top ranked ones. Advantage of the framework is that it considers both subset and individual feature's discrimination power, also it requires little information about the class label. A prototype of the proposed framework has been implemented and tested on the leukemia data set. The results have given positive support to the framework.