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Net2Image: A Network Representation Method for Identifying Cancer-Related Genes

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Part of the Lecture Notes in Computer Science book series (LNBI,volume 10330)


Although many machine learning algorithms have been proposed to identify cancer-related genes, their prediction accuracy is still limited due to the complex relationship between cancers and genes. To improve the prediction accuracy, many deep learning based tools have been developed, and they have shown their efficiency to handle complex relationships. To use those tools, a deliberate data representation method is indispensable, since majority tools only take those image-like data as inputs. In this study, we propose a novel network representation method, called Net2Image, to transfer topological networks into image-like datasets. The local topological information of individual vertices from six biomolecular networks and one DNA methylation dataset are encoded as \(80*6\) matrices. They are then employed as inputs to train the model for identifying cancer-related genes using TensorFlow. The numerical experiments show that the proposed method can achieve very high prediction accuracy, which outperforms many existing methods.


  • Deep learning
  • Biomolecular network
  • Cancer-related gene
  • Multiple data integration

B. Chen and Y. Jin—Equally contributing authors.

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  • DOI: 10.1007/978-3-319-59575-7_31
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  1. Altshuler, D., Daly, M., Kruglyak, L.: Guilt by association. Nat. Genet. 26(2), 135–137 (2000)

    CrossRef  Google Scholar 

  2. Chen, B., Wang, J., Li, M., Wu, F.X.: Identifying disease genes by integrating multiple data sources. BMC Med. Genomics 7(Suppl 2), S2 (2014)

    CrossRef  Google Scholar 

  3. Chen, B., Li, M., Wang, J., Shang, X., Wu, F.X.: A fast and high performance multiple data integration algorithm for identifying human disease genes. BMC Med. Genomics 8(Suppl 3), S2 (2015)

    CrossRef  Google Scholar 

  4. Vanunu, O., Magger, O., Ruppin, E., Shlomi, T., Sharan, R.: Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol. 6(1), e1000641 (2010)

    MathSciNet  CrossRef  Google Scholar 

  5. Ma, X., Lee, H., Wang, L., Sun, F.: CGI: a new approach for prioritizing genes by combining gene expression and protein-protein interaction data. Bioinformatics 23(2), 215–221 (2007)

    CrossRef  Google Scholar 

  6. Köhler, S., Bauer, S., Horn, D., Robinson, P.N.: Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. 82(4), 949–958 (2008)

    CrossRef  Google Scholar 

  7. Chen, Y., Wang, W., Zhou, Y., Shields, R., et al.: In silico gene prioritization by integrating multiple data sources. PLoS One 6(6), e21137 (2011)

    CrossRef  Google Scholar 

  8. Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)

    CrossRef  Google Scholar 

  9. Alipanahi, B., Delong, A., Weirauch, M.T., Frey, B.J.: Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat. Biotechnol. 33(8), 831–838 (2015)

    CrossRef  Google Scholar 

  10. Goh, K.I., Cusick, M.E., Valle, D., Childs, B., et al.: The human disease network. Proc. Natl. Acad. Sci. 104(21), 8685–8690 (2007)

    CrossRef  Google Scholar 

  11. Robertson, K.D.: DNA methylation and human disease. Nat. Rev. Genet. 6(8), 597–610 (2005)

    CrossRef  Google Scholar 

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This work was supported by the National Natural Science Foundation of China under Grant Nos. 61602386 and 61332014 and the Foundation of top university visiting for excellent youth scholars of Northwestern Polytechnical University.

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Correspondence to Xuequn Shang .

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Chen, B., Jin, Y., Shang, X. (2017). Net2Image: A Network Representation Method for Identifying Cancer-Related Genes. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham.

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  • Print ISBN: 978-3-319-59574-0

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