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A comparative analysis of biomarker selection techniques.

Dessì N, Pascariello E, Pes B - Biomed Res Int (2013)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Matematica e Informatica, Università degli Studi di Cagliari, Via Ospedale 72, 09124 Cagliari, Italy.

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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Joint evaluation of stability and predictive performance.
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fig2: Joint evaluation of stability and predictive performance.

Mentions: The methodology we adopt involves a single experimental setup to jointly evaluate both stability and predictive performance in the context of biomarker discovery. As illustrated in Figure 2, we extract from the original dataset D, with Z instances and N features (i.e., genes), a number P of reduced dataset Dk  (k = 1, 2,…, P), each containing f · Z (with f ∈ (0,1)) instances randomly drawn from D.


A comparative analysis of biomarker selection techniques.

Dessì N, Pascariello E, Pes B - Biomed Res Int (2013)

Joint evaluation of stability and predictive performance.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3842054&req=5

fig2: Joint evaluation of stability and predictive performance.
Mentions: The methodology we adopt involves a single experimental setup to jointly evaluate both stability and predictive performance in the context of biomarker discovery. As illustrated in Figure 2, we extract from the original dataset D, with Z instances and N features (i.e., genes), a number P of reduced dataset Dk  (k = 1, 2,…, P), each containing f · Z (with f ∈ (0,1)) instances randomly drawn from D.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Matematica e Informatica, Università degli Studi di Cagliari, Via Ospedale 72, 09124 Cagliari, Italy.

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.

Show MeSH