Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Screening a Case Base for Stroke Disease Detection Read chapter Read first chapter

2016 | OriginalPaper | Chapter

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

Authors : Alejandro Sánchez Medina, Alberto Gil Pichardo, Jose Manuel García-Heredia, María Martínez-Ballesteros

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Biclustering of Gene Expression Data Based on SimUI Semantic Similarity Measure
next chapter Extending Probabilistic Encoding for Discovering Biclusters in Gene Expression Data
Literature
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)CrossRef
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)CrossRef
3.
Kharya, S.: Using data mining techniques for diagnosis and prognosis of cancer disease. CoRR abs/1205.1923 (2012)
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)
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)CrossRef
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)
7.
Slonim, D.K., Yanai, I.: Getting started in gene expression microarray analysis. PLoS Comput. Biol. 5(10), e1000543 (2009)CrossRef
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)
9.
Geng, L., Hamilton, H.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 1–42 (2006)CrossRef
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)CrossRef
11.
Martínez-Ballesteros, M., Troncoso, A., Martínez-Álvarez, F., Riquelme, J.: Obtaining optimal quality measures for quantitative association rules. Neurocomputing 176, 36–47 (2016)CrossRef
12.
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
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)CrossRef
18.
Hanahan, D., Weinberg, R.A.: Hallmarks of cancer: the next generation. Cell 144(5), 646–674 (2015)CrossRef
Metadata
Title
Discovery of Genes Implied in Cancer by Genetic Algorithms and Association Rules
Authors
Alejandro Sánchez Medina
Alberto Gil Pichardo
Jose Manuel García-Heredia
María Martínez-Ballesteros
Copyright Year
2016
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-319-32033-5

Electronic ISBN: 978-3-319-32034-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-32034-2_58

Premium Partner

    Image Credits