Data Mining in Pathway Analysis for Gene Expression

被引：2

作者：

AlAjlan, Amani ^{[1
]}

Badr, Ghada ^{[1
,2
]}

机构：

[1] King Saud Univ, Coll Comp & Informat Sci, Riyadh, Saudi Arabia

[2] IRI City Sci Res & Technol Applicat, Alex, Egypt

来源：

ADVANCES IN DATA MINING: APPLICATIONS AND THEORETICAL ASPECTS, ICDM 2015 | 2015年 / 9165卷

关键词：

Pathway analysis; Gene expression; Clustering; Classification; Feature selection; RANDOM FORESTS CLASSIFICATION; MICROARRAY; CLUSTERS;

D O I：

10.1007/978-3-319-20910-4_6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Single gene analysis looks to a single gene at a time and its relation to a specific phenotype such as cancer development. However, pathway analysis simplifies the analysis by focusing on group of genes at a time that involve in the same biological process. Pathway analysis has useful applications such as discovering diseases, diseases prevention and drug development. Different data mining approaches can be applied in pathway analysis. In this paper, we overview different pathway analysis techniques in analyzing gene expression and propose a classification for them. Pathway analysis can be classified into: detecting significant pathways and discovering new pathways. In addition, we summarize different data mining techniques that are used in pathway analysis.

引用

页码：69 / 77

页数：9

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