Personalized modeling based gene selection for microarray data analysis

Authors:
Yingjie Hu;Qun Song;Nikola Kasabov
Affiliations:
The Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand;The Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand;The Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, New Zealand
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

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.