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A Hybrid Computational Method for the Discovery of Novel Reproduction-Related Genes

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Listed:
  • Lei Chen
  • Chen Chu
  • Xiangyin Kong
  • Guohua Huang
  • Tao Huang
  • Yu-Dong Cai

Abstract

Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations.

Suggested Citation

  • Lei Chen & Chen Chu & Xiangyin Kong & Guohua Huang & Tao Huang & Yu-Dong Cai, 2015. "A Hybrid Computational Method for the Discovery of Novel Reproduction-Related Genes," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-15, March.
    • Handle: RePEc:plo:pone00:0117090
    DOI: 10.1371/journal.pone.0117090
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    References listed on IDEAS

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    4. Lele Hu & Tao Huang & Xiaohe Shi & Wen-Cong Lu & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Predicting Functions of Proteins in Mouse Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-10, January.
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