Abstract
Gene expression ratios obtained from microarray images are strongly affected by the algorithms used to process them as well as by the quality of the images. Hundreds of spots often suffer from quality problems caused by the manufacturing process and many must be discarded because of lack of reliability. Recently, several computational models have been proposed in the literature to identify defective spots, including the powerful Support Vector Machines (SVMs). In this paper we propose to use different strategies based on aggregation methods to classify the spots according to their quality. On one hand we apply an ensemble of classifiers, in particular three boosting methods, namely Discrete, Real and Gentle AdaBoost. As we use a public dataset which includes the subjective labeling criteria of three human experts, we also evaluate different ways of modeling consensus between the experts. We show that for this problem ensembles achieve improved classification accuracies over alternative state-of-the-art methods.
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References
Schena, M., Shalon, D., Davis, R.W., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complementary cDNA microarray. Science 270, 467–470 (1995)
Lashkari, D.A., DeRisi, J.L., McCusker, J.H., Namath, A.F., Gentile, C., Hwang, S.Y., et al.: Yeast microarrays for genome wide parallel genetic and gene expression analysis. Proc. Natl. Acad. Sci. USA 94(24), 13057–13062 (1997)
Eisen, M.B., Brown, P.O.: DNA arrays for analysis of gene expression. Meth. Enzymol. 303, 179–205 (1999)
Alizadeh, A.A., Eisen, M.B., Davis, E.E., Ma, C., Lossos, I.S., Rosenwald, A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769), 503–511 (2000)
Bengtsson, A., Bengtsson, H.: Microarray image analysis: background estimation using quantile and morphological filters. BMC Bioinformatics 7(96), 1–15 (2006)
Chen, T.B., Lu, H.H.S., Lee, Y.S., Lan, H.J.: Segmentation of cDNA microarray images by kernel density estimation. Journal of Biomedical Informatics 41, 1021–1027 (2008)
Giannakeas, N., Fotiadis, D.I.: An automated method for gridding and clustering-based segmentation of cDNA microarray images. Comput. Med. Imaging Graph. 33, 40–49 (2009)
Bariamis, D., Maroulis, D., Iakovidis, D.: Unsupervised SVM-based gridding for DNA microarray images. Comput. Med. Imaging Graph. (2009)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
Sturn, A., Quackenbush, J., Trajanoski, Z.: Genesis: cluster analysis of microarray data. Bioinformatics 18(1), 207–208 (2002)
Chopra, P., Kang, J., Yang, J., Cho, H.J., Kim, H.S., Lee, M.G.: Microarray data mining using landmark gene-guided clustering. BMC Bioinformatics 9(92), 1–13 (2008)
Peterson, L.E., Coleman, M.A.: Logistic ensembles of Random Spherical Linear Oracles for microarray classification. Int. J. Data Min. Bioinform. 3(4), 297–382 (2009)
Axon GenePix Pro 7.1., http://www.moleculardevices.com
Eisen, M.: Scanalyze (1999), http://rana.lbl.gov/EisenSoftware.html
Ruosaari, S., Hollmen, J.: Image analysis for detecting faulty spots from microarray images. In: Lange, S., Satoh, K., Smith, C. (eds.) DS 2002. LNCS, vol. 2534, pp. 259–266. Springer, Heidelberg (2002)
Hautaniemi, S., Edgren, H., Vesanen, P., Wolf, M., Jrvinen, A.K., Yli Harja, O., et al.: A novel strategy for microarray quality control using bayesian networks. Bioinformatics 19(16), 2031–2038 (2003)
Bicego, M., Martínez, M.D.R., Murino, V.: A supervised data-driven approach for microarray spot quality classification. Pattern Anal. Applic. 8, 181–187 (2005)
Vapnik, V.: The nature of statistical learningn theory. Springer, Heidelberg (1995)
Liu, X., Zhang, L., Li, M., Zhang, H., Wang, D.: Boosting image classification with LDA-based feature combination for digital photograph management. Pattern Recognition 38(6), 887–901 (2005)
Baluja, S., Rowley, H.A.: Boosting sex identification performance. International Journal of Computer Vision 71(1), 111–119 (2007)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer, Heidelberg (2009)
Valiant, L.G.: A theory of the learnable. Communications of the ACM 27, 1134–1142 (1984)
Schapire, R.E.: The Strength of Weak Learnability. Mach. Learn. 5(2), 197–227 (1990)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Friedman, J.H., Hastie, T., Tibshirani, R.: Additive logistic regression: A statistical view of boosting. Annals of Statistics 28, 337–407 (2000)
Benediktsson, J.A., Swain, P.H.: Consensus theoretic classification methods. IEEE Trans. Systems, Man Cybernet. 22, 688–704 (1992)
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Larese, M.G., Granitto, P.M., Gómez, J.C. (2010). Learning to Discover Faulty Spots in cDNA Microarrays. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_23
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DOI: https://doi.org/10.1007/978-3-642-16952-6_23
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