A multivariate algorithm for gene selection based on the nearest neighbor probability

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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. © 2009 Springer Berlin Heidelberg.

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Ferrari, E., & Muselli, M. (2009). A multivariate algorithm for gene selection based on the nearest neighbor probability. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5488 LNBI, pp. 123–131). https://doi.org/10.1007/978-3-642-02504-4_11

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