Personalized Modeling Based Gene Selection for Microarray Data Analysis
This paper presents a novel gene selection method based on personalized modeling. Identifying a compact set of genes from gene expression data is a critical step in bioinformatics research. Personalized modeling is a recently introduced technique for constructing clinical decision support systems. In this paper we have provided a comparative study using the proposed Personalized Modeling based Gene Selection method (PMGS) on two benchmark microarray datasets (Colon cancer and Central Nervous System cancer data). The experimental results show that our method is able to identify a small number of informative genes which can lead to reproducible and acceptable predictive performance without expensive computational cost. These genes are of importance for specific groups of people for cancer diagnosis and prognosis.
KeywordsGene Selection Clinical Decision Support System Microarray Data Analysis Informative Gene Wrap Method
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- 1.Nevins, J.R., Huang, E.S., Dressman, H., Pittman, J., Huang, A.T., West, M.: Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction. Human Molecular Genetics 12(2), R153–R157 (2003)Google Scholar
- 3.Hu, Y., Kasabov, N.: ntology-based framework for personalized diagnosis and prognosis of cancer based on gene expression data. In: ICONIP 2007 14th International Conference on Neural Information Processing, Kitakyushu City, Fukuoka, Japan, vol. 2, pp. 846–855 (2007)Google Scholar
- 7.Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci., USA 96, 6745–6750 (1999)Google Scholar