An Improved SOM-based Visualization Technique for DNA Microarray Data Analysis

被引：0
|
作者：
Patra, Jagdish C. [1 ]
Abraham, Jacob [1 ]
Meher, Pramod K. [2 ]
Chakraborty, Goutam [3 ]
机构：
[1] Nanyang Technol Univ, Sch Comp Engn, Singapore, Singapore
[2] Inst Infocomm Res, Dept Commun Syst, Singapore, Singapore
[3] Iwate Prefectural Univ, Dept Software & Informat Sci, Sugo, Japan
来源：
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 | 2010年
关键词：
SELF-ORGANIZING MAPS; GENE-EXPRESSION DATA; CDNA MICROARRAY; CLASS DISCOVERY; HUMAN CANCER; PATTERNS; IDENTIFICATION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Effective and meaningful visualization techniques are quite important for multidimensional DNA microarray gene expression data analysis. Elucidating the cluster properties of these multidimensional data are often complex. Patterns, hypotheses on the relationships, and ultimately of the function of the gene can be analyzed and visualized by non-linear reduction of the multidimensional data to a lower dimension. In this paper, an improved SOM visualization technique named Improved Side Intensity Modulated (ISIM) Self-Organizing Map (SOM) has been proposed and compared with other SOM based visualization techniques. On different datasets, ISIM-SOM is found to offer better cluster boundary, simplicity and clarity.
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页数：7
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