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http://dx.doi.org/10.5302/J.ICROS.2004.10.12.1172

Cancer-Subtype Classification Based on Gene Expression Data  

Cho Ji-Hoon (포항공과대학교 화학공학과)
Lee Dongkwon (LG화학)
Lee Min-Young (포항공과대학교 화학공학과)
Lee In-Beum (포항공과대학교 화학공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.10, no.12, 2004 , pp. 1172-1180 More about this Journal
Abstract
Recently, the gene expression data, product of high-throughput technology, appeared in earnest and the studies related with it (so-called bioinformatics) occupied an important position in the field of biological and medical research. The microarray is a revolutionary technology which enables us to monitor several thousands of genes simultaneously and thus to gain an insight into the phenomena in the human body (e.g. the mechanism of cancer progression) at the molecular level. To obtain useful information from such gene expression measurements, it is essential to analyze the data with appropriate techniques. However the high-dimensionality of the data can bring about some problems such as curse of dimensionality and singularity problem of matrix computation, and hence makes it difficult to apply conventional data analysis methods. Therefore, the development of method which can effectively treat the data becomes a challenging issue in the field of computational biology. This research focuses on the gene selection and classification for cancer subtype discrimination based on gene expression (microarray) data.
Keywords
gene expression; gene selection; classification; multivariate statistical method; machine learning;
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