DocumentCode :
680181
Title :
Differential coexpression analysis in gene modules level and its application to type 2 diabetes
Author :
Lin Yuan ; Wen Sha ; Jun Zhang ; Chun-Hou Zheng ; Jun-Feng Xia
Author_Institution :
Coll. of Electr. Eng. & Autom., Anhui Univ., Hefei, China
fYear :
2013
fDate :
18-21 Dec. 2013
Firstpage :
193
Lastpage :
196
Abstract :
More and more studies have shown many complex diseases are contributed jointly by alterations of numerous genes. In this paper, we propose a gene differential coexpression analysis algorithm in the level of gene sets and apply the algorithm to a publicly available type 2 diabetes (T2D) expression dataset. The experimental results on simulated data show that the new approach performed well. Moreover, we apply the new approach to clinical data, many additional discoveries can be found through our method.
Keywords :
diseases; genetics; T2D; diseases; gene differential coexpression analysis algorithm; gene module level; gene set level; type 2 diabetes expression dataset; Algorithm design and analysis; Biology; Correlation; Diabetes; Diseases; Noise; Standards; biweight midcorrelation; differential coexpression analysis; k-clique algorithm; threshold strategy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on
Conference_Location :
Shanghai
Type :
conf
DOI :
10.1109/BIBM.2013.6732487
Filename :
6732487
Link To Document :
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