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

Author

Listed:
  • Xiaobo Yang

    (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)

  • 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)

  • 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)

  • 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)

  • 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)

  • 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)

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.

Suggested Citation

  • Xiaobo Yang & Binghui Guo & Zhilong Mi & Ziqiao Yin & Jiahui Li & Zhiming Zheng, 2020. "Identifying vital genes of breast cancer through synergy network by part mutual information," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(06), pages 1-25, June.
    • Handle: RePEc:wsi:ijmpcx:v:31:y:2020:i:06:n:s0129183120500886
    DOI: 10.1142/S0129183120500886
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