On Knowledge-Based Gene Expression Data Analysis

被引：0

作者：

Arakelyan, Arsen ^{[1
]}

Aslanyan, Levon ^{[2
]}

Boyajyan, Anna ^{[1
]}

机构：

[1] Inst Mol Biol, Yerevan, Armenia

[2] Inst Informat & Automat Problems NAS RA, Yerevan, Armenia

来源：

2013 COMPUTER SCIENCE AND INFORMATION TECHNOLOGIES (CSIT) | 2013年

关键词：

Gene expression; probability distribution; classification; feature selection; functional pathway; HIGH-DIMENSION; MICROARRAY; PATTERNS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Nowadays, technology and technological diversity of the gene expression measurements raise new issues related to proper data analysis and accurate interpretation of results. Because gene expression measurement includes many complex steps and is performed on randomly selected cells from population, the measured value of gene expression is a stochastic variable, and may vary in wide range. Consequently, based on gene expression values it is hard to distinguish the genes associated with condition of interest from those none-associated. Thepresent articleis focused on theoretical justification of the mentioned problemand proposesa solutionthrough complementing of data-driven analysis with knowledge-based approaches.

引用

页数：5

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