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. 2013:2013:387673.
doi: 10.1155/2013/387673. Epub 2013 Nov 10.

A comparative analysis of biomarker selection techniques

Nicoletta Dessì  1 , Emanuele Pascariello, Barbara Pes
Affiliations

A comparative analysis of biomarker selection techniques

Nicoletta Dessì et al. Biomed Res Int. 2013.

Abstract

Feature selection has become the essential step in biomarker discovery from high-dimensional genomics data. It is recognized that different feature selection techniques may result in different set of biomarkers, that is, different groups of genes highly correlated to a given pathological condition, but few direct comparisons exist which quantify these differences in a systematic way. In this paper, we propose a general methodology for comparing the outcomes of different selection techniques in the context of biomarker discovery. The comparison is carried out along two dimensions: (i) measuring the similarity/dissimilarity of selected gene sets; (ii) evaluating the implications of these differences in terms of both predictive performance and stability of selected gene sets. As a case study, we considered three benchmarks deriving from DNA microarray experiments and conducted a comparative analysis among eight selection methods, representatives of different classes of feature selection techniques. Our results show that the proposed approach can provide useful insight about the pattern of agreement of biomarker discovery techniques.

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Figures

Figure 1
Figure 1
Similarity evaluation.
Figure 2
Figure 2
Joint evaluation of stability and predictive performance.
Figure 3
Figure 3
Colon dataset: stability versus number of genes.
Figure 4
Figure 4
Leukemia dataset: stability versus number of genes.
Figure 5
Figure 5
Prostate dataset: stability versus number of genes.
Figure 6
Figure 6
Colon dataset: AUC versus number of genes.
Figure 7
Figure 7
Leukemia dataset: AUC versus number of genes.
Figure 8
Figure 8
Prostate dataset: AUC versus number of genes.
See this image and copyright information in PMC

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