Abstract
The application of microarray technology to the diagnosis of cancer has been a challenge for computational techniques because the datasets obtained have high dimension and a few examples. In this paper two computational techniques are applied to tumor datasets in order to carry out the task of diagnosis of cancer (classification task) and identifying the most promising candidates among large list of genes (gene prioritization). Both techniques obtain good classification results but only one provides a ranking of genes as additional information and thus, more interpretable models, being more suitable for jointly addressing both tasks.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. U.S.A. 96, 6745–6750 (1999)
Ben-Dor, A., Bruhn, L., Friedman, N., Nachman, I., Schummer, M., Yakhini, Z.: Tissue classification with gene expression profiles. J. Comput. Biol. 7(3–4), 559–583 (2004)
Bonissone, P.P., Cadenas, J.M., Garrido, M.C., Díaz-Valladares, R.A.: A fuzzy random forest. Int. J. Approximate Reasoning 51(7), 729–747 (2010)
Brandley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Cadenas, J.M., Garrido, M.C., Martínez, R.: Feature subset selection filter-wrapper based on low quality data. Expert Syst. Appl. 40, 1–10 (2013)
Cadenas, J.M., Garrido, M.C., Martínez, R., Bonissone, P.P.: Extending information processing in a fuzzy random forest ensemble. Soft Comput. 16(5), 845–861 (2012)
J.M. Cadenas, M.C. Garrido, R. Martínez, P.P. Bonissone, Ofp\(\_\)class: a hybrid method to generate optimized fuzzy partitions for classification. Soft Comput. 16(4), 667–682 (2012)
Clarke, P.A., George, M., Cunningham, D., Swift, I., Workman, P.: Analysis of tumor gene expression following chemotherapeutic treatment of patients with bowel cancer. Nat. Genet. 23(3), 39–39 (1999)
Dagliyan, O., Uney-Yuksektepe, F., Kavakli, I.H., Turkay, M.: Optimization based tumor classification from microarray gene expression data. PLoS ONE 6(2), e14579 (2011)
DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3), 837–845 (1988)
Diaz-Uriarte, R., Alvarez de Andrés, S.: Gene selection and classification of microarray data using random forest. BMC Bioinform. 7(3), (2006)
Duval, B., Hao, J.K.: Advances in metaheuristics for gene selection and classification of microarray data. Briefings Bioinform. 11(1), 127–141 (2010)
Genuer, R., Poggi, J.M., Tuleau-Malot, C.: Variable selecting using random forest. Pattern Recogn. Lett. 31(14), 2225–2236 (2010)
Ghoraia, S., Mukherjeeb, A., Duttab, P.K.: Gene expression data classification by VVRKFA. Procedia Technol. 4, 330–335 (2012)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.: P, Coller H., Loh M., Downing J. R., Caligiuri M. A., Bloomfield C. D., Lander E.S.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1), 29–36 (1982)
Mukhopadhyaya, A., Maulikb, U.: Towards improving fuzzy clustering using support vector machine: Application to gene expression data. Pattern Recogn. 42(11), 2744–2763 (2009)
Nitsch D., Gonzalves J. P., Ojeda F., De Moor B., Moreau Y.: Candidate gene prioritization by network analysis of differential expression using machine learning approaches. BMC Bioinform. 11(460), (2010)
Saeys, Y., Inza, I., Larraaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Singh D., Febbo P. G., Ross K., Jackson D. G. et all: Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1(2), 203–209 (2002)
Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)
Acknowledgments
Supported by the projects TIN2011-27696-C02-01 and TIN2011-27696-C02-02 of the Ministry of Economy and Competitiveness of Spain. Thanks also to “Agencia de Ciencia y Tecnología de la Región de Murcia” (Spain) for the support given to Raquel Martínez by the scholarship program FPI.
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
Cadenas, J.M., Garrido, M.C., Martínez, R., Pelta, D., Bonissone, P.P. (2016). Gene Priorization for Tumor Classification Using an Embedded Method. In: Madani, K., Dourado, A., Rosa, A., Filipe, J., Kacprzyk, J. (eds) Computational Intelligence. IJCCI 2013. Studies in Computational Intelligence, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-23392-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-23392-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23391-8
Online ISBN: 978-3-319-23392-5
eBook Packages: EngineeringEngineering (R0)