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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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Prostate dataset: stability versus number of genes.
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Related In: Results  -  Collection


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fig5: Prostate dataset: stability versus number of genes.

Mentions: After evaluating the degree of similarity/dissimilarity among the outcomes of different ranking methods, we empirically examined the implications of these differences in terms of both stability and predictive performance of selected gene subsets. In Figures 3, 4, and 5 we summarize, respectively, for Colon, Leukemia, and Prostate datasets, the results of stability analysis on gene subsets of increasing size. As explained in Section 2.2, the stability value was obtained, for a given ranking method, as the average similarity (I-overlap) among the gene subsets selected by this method from a number P = 20 of reduced datasets randomly drawn from the original dataset.


A comparative analysis of biomarker selection techniques.

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

Prostate dataset: stability versus number of genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Prostate dataset: stability versus number of genes.
Mentions: After evaluating the degree of similarity/dissimilarity among the outcomes of different ranking methods, we empirically examined the implications of these differences in terms of both stability and predictive performance of selected gene subsets. In Figures 3, 4, and 5 we summarize, respectively, for Colon, Leukemia, and Prostate datasets, the results of stability analysis on gene subsets of increasing size. As explained in Section 2.2, the stability value was obtained, for a given ranking method, as the average similarity (I-overlap) among the gene subsets selected by this method from a number P = 20 of reduced datasets randomly drawn from the original dataset.

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