%0 Journal Article
	%A Liping Jing and  Michael K. Ng and  Tieyong Zeng
	%D 2010
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 38, 2010
	%T Novel Hybrid Method for Gene Selection and Cancer Prediction
	%U https://publications.waset.org/pdf/8670
	%V 38
	%X Microarray data profiles gene expression on a whole
genome scale, therefore, it provides a good way to study associations
between gene expression and occurrence or progression of cancer.
More and more researchers realized that microarray data is helpful
to predict cancer sample. However, the high dimension of gene
expressions is much larger than the sample size, which makes this
task very difficult. Therefore, how to identify the significant genes
causing cancer becomes emergency and also a hot and hard research
topic. Many feature selection algorithms have been proposed in
the past focusing on improving cancer predictive accuracy at the
expense of ignoring the correlations between the features. In this
work, a novel framework (named by SGS) is presented for stable gene
selection and efficient cancer prediction . The proposed framework
first performs clustering algorithm to find the gene groups where
genes in each group have higher correlation coefficient, and then
selects the significant genes in each group with Bayesian Lasso and
important gene groups with group Lasso, and finally builds prediction
model based on the shrinkage gene space with efficient classification
algorithm (such as, SVM, 1NN, Regression and etc.). Experiment
results on real world data show that the proposed framework often
outperforms the existing feature selection and prediction methods,
say SAM, IG and Lasso-type prediction model.
	%P 258 - 265