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.
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Hu, Y., Song, Q., Kasabov, N. (2009). Personalized Modeling Based Gene Selection for Microarray Data Analysis. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_148
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DOI: https://doi.org/10.1007/978-3-642-02490-0_148
Publisher Name: Springer, Berlin, Heidelberg
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