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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
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)
Kharya, S.: Using data mining techniques for diagnosis and prognosis of cancer disease. CoRR abs/1205.1923 (2012)
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)
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)
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)
Slonim, D.K., Yanai, I.: Getting started in gene expression microarray analysis. PLoS Comput. Biol. 5(10), e1000543 (2009)
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)
Geng, L., Hamilton, H.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 1–42 (2006)
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)
Martínez-Ballesteros, M., Troncoso, A., Martínez-Álvarez, F., Riquelme, J.: Obtaining optimal quality measures for quantitative association rules. Neurocomputing 176, 36–47 (2016)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)
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)
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)
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)
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)
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)
Hanahan, D., Weinberg, R.A.: Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2015)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Medina, A.S., Pichardo, A.G., García-Heredia, J.M., Martínez-Ballesteros, . (2016). Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-32034-2_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32033-5
Online ISBN: 978-3-319-32034-2
eBook Packages: Computer ScienceComputer Science (R0)