Comparing correlation coefficients as dissimilarity measures for cancer classification in gene expression data

Jaskowiak, Pablo A., and Campello, Ricardo J.G.B. (2011) Comparing correlation coefficients as dissimilarity measures for cancer classification in gene expression data. In: Proceedings of the 6th Brazilian Symposium on Bioinformatics, pp. 1-8. From: BSB 2011: 6th Brazilian Symposium on Bioinformatics, 10-12 August 2011, Brasilia, Brazil.

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Abstract

An important analysis performed in gene expression data is sample classification, e.g., the classification of different types or subtypes of cancer. Different classifiers have been employed for this challenging task, among which the k-Nearest Neighbors (kNN) classifier stands out for being at the same time very simple and highly flexible in terms of discriminatory power. Although the choice of a dissimilarity measure is essential to kNN, little effort has been undertaken to evaluate how this choice affects its performance in cancer classification. To this extent, we compare seven correlation coefficients for cancer classification using kNN. Our comparison suggests that a recently introduced correlation may perform better than commonly used measures. We also show that correlation coefficients rarely considered can provide competitive results when compared to widely used dissimilarity measures.

Item ID: 47967
Item Type: Conference Item (Research - E1)
ISSN: 2178-5120
Funders: CNPq, Brazil, FAPESP
Date Deposited: 16 May 2017 04:02
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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