Abstract
Experiments performed with DNA microarrays have very often the aim of retrieving a subset of genes involved in the discrimination between two physiological or pathological states (e.g. ill/healthy). Many methods have been proposed to solve this problem, among which the Signal to Noise ratio (S2N) [5] and SVM-RFE [6]. Recently, the complementary approach to RFE, called Recursive Feature Addition (RFA), has been successfully adopted. According to this approach, at each iteration the gene which maximizes a proper ranking function φ is selected, thus producing an ordering among the considered genes. In this paper an RFA method based on the nearest neighbor probability, named NN-RFA, is described and tested on some real world problems regarding the classification of human tissues. The results of such simulations show the ability of NN-RFA in retrieving a correct subset of genes for the problems at hand.
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References
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Liu, Q., Sung, A.H.: Recursive feature addition for gene selection. In: Proceedings of IJCNN 2006, Vancouver, BC, Canada, pp. 1360–1367 (2003)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience, Hoboken (2000)
Ruffino, F., Muselli, M., Valentini, G.: Gene expression modeling through positive Boolean functions. International Journal of Approximate Reasoning 47, 97–108 (2008)
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
Guyon, I., et al.: Gene selection for cancer classification using support vector machines. Machine learning 46(1–3), 389–422 (2002)
Alon, U., et al.: Broad patterns of gene expressions revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Science USA 96, 6745–6750 (1999)
Alizadeh, A.A., et al.: Different types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
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Ferrari, E., Muselli, M. (2009). A Multivariate Algorithm for Gene Selection Based on the Nearest Neighbor Probability. In: Masulli, F., Tagliaferri, R., Verkhivker, G.M. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2008. Lecture Notes in Computer Science(), vol 5488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02504-4_11
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DOI: https://doi.org/10.1007/978-3-642-02504-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02503-7
Online ISBN: 978-3-642-02504-4
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