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doi:10.3724/SP.J.1087.2011.02774

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Abstract��In order to find possible diagnostic genes that may typically assist in disease diagnosis, clustering technologies are always used to analyze gene expression data. Molecular-kinetic-theory-based clustering approach is a new and effective clustering technique. It finds data clusters by following the molecular kinetic mechanism. This dynamic clustering approach does not require presetting the number of clusters and can be used to estimate the number of clusters. The authors applied the method on gene expression data to estimate the number of clusters and possible diagnostic genes according to relevant clustering criteria. The simulation results and analysis verify the good knowledge discovery ability of this approach.

Key words�� clustering molecular kinetics interaction gene expression data mining knowledge discovery

�ո��: 2011-03-10 ��: 2011-10-11

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��Ȼ��ѧ��Ŀ(61073099);��У��ҵ��ר��ʽ��Ŀ(103.1.2 E022050205;ZYGX2009J058)

ͨѶ��: ��� E-mail: myronsaga1@sohu.com

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��� . �·��Ӷ��ѧ��ݾ��෨�ڻ��ϵ�Ӧ��[J]. ��Ӧ��, 2011, 31(10): 2774-2777. LI Jun-lin FU Hong-guang. Applications of molecular-kinetic-theory-based clustering approach on gene expression data. Journal of Computer Applications, 2011, 31(10): 2774-2777.

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http://www.joca.cn/CN/10.3724/SP.J.1087.2011.02774 �� http://www.joca.cn/CN/Y2011/V31/I10/2774