DMCA
Comparing Correlation Coefficients as Dissimilarity Measures for Cancer Classification in Gene Expression Data (2011)
| Venue: | BRAZILIAN SYMPOSIUM ON BIOINFORMATICS (BSB) |
BibTeX
@MISC{Jaskowiak11comparingcorrelation,
author = {Pablo A. Jaskowiak and Ricardo J. G. B. Campello},
title = {Comparing Correlation Coefficients as Dissimilarity Measures for Cancer Classification in Gene Expression Data},
year = {2011}
}
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Abstract
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.
