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Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules

  • Alejandro Sánchez Medina
  • Alberto Gil Pichardo
  • Jose Manuel García-Heredia
  • María Martínez-BallesterosEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)

Abstract

This work proposes a methodology to identify genes highly related with cancer. In particular, a multi-objective evolutionary algorithm named CANGAR is applied to obtain quantitative association rules. This kind of rules are used to identify dependencies between genes and their expression levels. Hierarchical cluster analysis, fold-change and review of the literature have been considered to validate the relevance of the results obtained. The results show that the reported genes are consistent with prior knowledge and able to characterize cancer colon patients.

Keywords

Data mining Association rules Gene expression Cancer 

Notes

Acknowledgments

The financial support from the Spanish Ministry of Science and Technology, projects TIN2011-28956-C02-02 and TIN2014-55894-C2-1-R, and from the Junta de Andalucia, P11-TIC-7528 and P12-TIC-1728, is acknowledged.

References

  1. 1.
    Ellis, L., Woods, L.M., Estve, J., Eloranta, S., Coleman, M.P., Rachet, B.: Cancer incidence, survival and mortality: explaining the concepts. Int. J. Cancer 135(8), 1774–1782 (2014)CrossRefGoogle Scholar
  2. 2.
    López-Abente, G., Aragonés, N., Pérez-Gómez, B., Pollán, M., García-Pérez, J., Ramis, R., Fernández-Navarro, P.: Time trends in municipal distribution patterns of cancer mortality in spain. BMC Cancer 14(1), 1–15 (2014)CrossRefGoogle Scholar
  3. 3.
    Kharya, S.: Using data mining techniques for diagnosis and prognosis of cancer disease. CoRR abs/1205.1923 (2012)Google Scholar
  4. 4.
    Sarvestani, A., Safavi, A., Parandeh, N., Salehi, M.: Predicting breast cancer survivability using data mining techniques. In: 2nd International Conference on Software Technology and Engineering (ICSTE) 2010, vol. 2, pp. 227–231 (2010)Google Scholar
  5. 5.
    Lopez, F., Cuadros, M., Cano, C., Concha, A., Blanco, A.: Biomedical application of fuzzy association rules for identifying breast cancer biomarkers. Med. Biol. Eng. Comput. 50(9), 981–990 (2012)CrossRefGoogle Scholar
  6. 6.
    Tang, J.Y., Chuang, L.Y., Hsi, E., Lin, Y.D., Yang, C.H., Chang, H.W.: Identifying the association rules between clinicopathologic factors and higher survival performance in operation-centric oral cancer patients using the apriori algorithm. Biomed. Res. Int. 2013, 7 (2013)Google Scholar
  7. 7.
    Slonim, D.K., Yanai, I.: Getting started in gene expression microarray analysis. PLoS Comput. Biol. 5(10), e1000543 (2009)CrossRefGoogle Scholar
  8. 8.
    Martínez-Ballesteros, M., Troncoso, A., Martínez-Álvarez, F., Riquelme, J.C.: Improving a multi-objective evolutionary algorithm to discover quantitative association rules. Knowl. Inf. Syst. 1–29 (2015)Google Scholar
  9. 9.
    Geng, L., Hamilton, H.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 1–42 (2006)CrossRefGoogle Scholar
  10. 10.
    Martínez-Ballesteros, M., Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C.: Quantitative association rules applied to climatological time series forecasting. In: Corchado, Emilio, Yin, Hujun (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 284–291. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Martínez-Ballesteros, M., Troncoso, A., Martínez-Álvarez, F., Riquelme, J.: Obtaining optimal quality measures for quantitative association rules. Neurocomputing 176, 36–47 (2016)CrossRefGoogle Scholar
  12. 12.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)CrossRefGoogle Scholar
  13. 13.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  14. 14.
    Tsukamoto, S., Ishikawa, T., Iida, S., Ishiguro, M., Mogushi, K., Mizushima, H., Uetake, H., Tanaka, H., Sugihara, K.: Clinical significance of osteoprotegerin expression in human colorectal cancer. Clin. Cancer Res. 17(8), 2444–2450 (2011)CrossRefGoogle Scholar
  15. 15.
    Hu, R., Zuo, Y., Zuo, L., Liu, C., Zhang, S., Wu, Q., Zhou, Q., Gui, S., Wei, W., Wang, Y.: Klf4 expression correlates with the degree of differentiation in colorectal cancer. Gut Liver 5(2), 154 (2011)CrossRefGoogle Scholar
  16. 16.
    Kreso, A., van Galen, P., Pedley, N.M., Lima-Fernandes, E., Frelin, C., Davis, T., Cao, L., Baiazitov, R., Du, W., Sydorenko, N., Moon, Y.C., Gibson, L., Wang, Y., Leung, C., Iscove, N.N., Arrowsmith, C.H., Szentgyorgyi, E., Gallinger, S., Dick, J.E., O’Brien, C.A.: Self-renewal as a therapeutic target in human colorectal cancer. Nat. Med. 20(1), 29–36 (2014)CrossRefGoogle Scholar
  17. 17.
    Martínez-Ballesteros, M., Martínez-Álvarez, F., Lora, A.T., Riquelme, J.C.: Selecting the best measures to discover quantitative association rules. Neurocomputing 126, 3–14 (2014)CrossRefGoogle Scholar
  18. 18.
    Hanahan, D., Weinberg, R.A.: Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alejandro Sánchez Medina
    • 1
  • Alberto Gil Pichardo
    • 1
  • Jose Manuel García-Heredia
    • 2
  • María Martínez-Ballesteros
    • 3
    Email author
  1. 1.University of SevillaSevilleSpain
  2. 2.Department of Vegetal Biochemistry and Molecular BiologyUniversity of SevilleSevilleSpain
  3. 3.Department of Computer ScienceUniversity of SevilleSevilleSpain

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