Research PaperNo Access

Identifying vital genes of breast cancer through synergy network by part mutual information

Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, P. R. China

LMIB and School of Mathematics and Systems Science, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

E-mail Address: yangxb@buaa.edu.cn

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Binghui Guo

Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, P. R. China

LMIB and School of Mathematics and Systems Science, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

E-mail Address: guobinghui@buaa.edu.cn

Corresponding author.

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Zhilong Mi

Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, P. R. China

LMIB and School of Mathematics and Systems Science, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

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Ziqiao Yin

Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, P. R. China

LMIB and School of Mathematics and Systems Science, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

Shenyuan Honors College, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

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Jiahui Li

Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, P. R. China

LMIB and School of Mathematics and Systems Science, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

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, and

Zhiming Zheng

Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

Peng Cheng Laboratory, Shenzhen, Guangdong Province 518055, P. R. China

LMIB and School of Mathematics and Systems Science, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, P. R. China

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https://doi.org/10.1142/S0129183120500886Cited by:0 (Source: Crossref)

Abstract

Breast cancer is a common malignant tumor of which pathogenic genes are widely studied. Since gene pairs are considered as biomarkers to identify cancer patients, in this paper, we use information theory to study the collaboration features of gene pairs. The measure of synergy based on mutual information (MI) is introduced to determine whether genes collaborate with each other in breast cancer. Part mutual information (PMI) is introduced to further select collaborative genes and construct a synergy network, which overcomes the shortage of MI. Furthermore, a dual network of synergy network is constructed and structural indices are calculated to identify vital genes. By decision tree and support vector machine, synergy is considered as a suitable index and dual network with PMI improves the accuracy of cancer identification. This method can be extended to identify other biological phenomenon and find collaborative genes as biomarkers.

Keywords:

PACS: 02.50.−r, 89.70.+c, 89.75.−k

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