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ccSVM: correcting Support Vector Machines for confounding factors in biological data classification.

Li L, Rakitsch B, Borgwardt K - Bioinformatics (2011)

Bottom Line: However, it is unclear how to correct for confounding factors such as population structure, age or gender or experimental conditions in Support Vector Machine classification.In this article, we present a Support Vector Machine classifier that can correct the prediction for observed confounding factors.In our experiments, our confounder correcting SVM (ccSVM) improves tumor diagnosis based on samples from different labs, tuberculosis diagnosis in patients of varying age, ethnicity and gender, and phenotype prediction in the presence of population structure and outperforms state-of-the-art methods in terms of prediction accuracy.

View Article: PubMed Central - PubMed

Affiliation: Machine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, Tübingen, Germany. limin.li@tuebingen.mpg.de

ABSTRACT

Motivation: Classifying biological data into different groups is a central task of bioinformatics: for instance, to predict the function of a gene or protein, the disease state of a patient or the phenotype of an individual based on its genotype. Support Vector Machines are a wide spread approach for classifying biological data, due to their high accuracy, their ability to deal with structured data such as strings, and the ease to integrate various types of data. However, it is unclear how to correct for confounding factors such as population structure, age or gender or experimental conditions in Support Vector Machine classification.

Results: In this article, we present a Support Vector Machine classifier that can correct the prediction for observed confounding factors. This is achieved by minimizing the statistical dependence between the classifier and the confounding factors. We prove that this formulation can be transformed into a standard Support Vector Machine with rescaled input data. In our experiments, our confounder correcting SVM (ccSVM) improves tumor diagnosis based on samples from different labs, tuberculosis diagnosis in patients of varying age, ethnicity and gender, and phenotype prediction in the presence of population structure and outperforms state-of-the-art methods in terms of prediction accuracy.

Availability: A ccSVM-implementation in MATLAB is available from http://webdav.tuebingen.mpg.de/u/karsten/Forschung/ISMB11_ccSVM/.

Contact: limin.li@tuebingen.mpg.de; karsten.borgwardt@tuebingen.mpg.de.

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Related in: MedlinePlus

Genes are sorted according to the weight vector of the ccSVM (blue dashed line) and according to the weight vector of the standard SVM (green line). The correlation coefficient between each gene expression level and lab membership is calculated. The averaged absolute correlation coefficient of the top i genes is plotted for gene i.
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Figure 1: Genes are sorted according to the weight vector of the ccSVM (blue dashed line) and according to the weight vector of the standard SVM (green line). The correlation coefficient between each gene expression level and lab membership is calculated. The averaged absolute correlation coefficient of the top i genes is plotted for gene i.

Mentions: For the synthetic data, we can see from Figure 1 that the ccSVM assigns large weights to genes that weakly correlate with the lab membership while the standard SVM assigns the weights without paying attention to the correlation to the lab membership.Fig. 1.


ccSVM: correcting Support Vector Machines for confounding factors in biological data classification.

Li L, Rakitsch B, Borgwardt K - Bioinformatics (2011)

Genes are sorted according to the weight vector of the ccSVM (blue dashed line) and according to the weight vector of the standard SVM (green line). The correlation coefficient between each gene expression level and lab membership is calculated. The averaged absolute correlation coefficient of the top i genes is plotted for gene i.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117385&req=5

Figure 1: Genes are sorted according to the weight vector of the ccSVM (blue dashed line) and according to the weight vector of the standard SVM (green line). The correlation coefficient between each gene expression level and lab membership is calculated. The averaged absolute correlation coefficient of the top i genes is plotted for gene i.
Mentions: For the synthetic data, we can see from Figure 1 that the ccSVM assigns large weights to genes that weakly correlate with the lab membership while the standard SVM assigns the weights without paying attention to the correlation to the lab membership.Fig. 1.

Bottom Line: However, it is unclear how to correct for confounding factors such as population structure, age or gender or experimental conditions in Support Vector Machine classification.In this article, we present a Support Vector Machine classifier that can correct the prediction for observed confounding factors.In our experiments, our confounder correcting SVM (ccSVM) improves tumor diagnosis based on samples from different labs, tuberculosis diagnosis in patients of varying age, ethnicity and gender, and phenotype prediction in the presence of population structure and outperforms state-of-the-art methods in terms of prediction accuracy.

View Article: PubMed Central - PubMed

Affiliation: Machine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, Tübingen, Germany. limin.li@tuebingen.mpg.de

ABSTRACT

Motivation: Classifying biological data into different groups is a central task of bioinformatics: for instance, to predict the function of a gene or protein, the disease state of a patient or the phenotype of an individual based on its genotype. Support Vector Machines are a wide spread approach for classifying biological data, due to their high accuracy, their ability to deal with structured data such as strings, and the ease to integrate various types of data. However, it is unclear how to correct for confounding factors such as population structure, age or gender or experimental conditions in Support Vector Machine classification.

Results: In this article, we present a Support Vector Machine classifier that can correct the prediction for observed confounding factors. This is achieved by minimizing the statistical dependence between the classifier and the confounding factors. We prove that this formulation can be transformed into a standard Support Vector Machine with rescaled input data. In our experiments, our confounder correcting SVM (ccSVM) improves tumor diagnosis based on samples from different labs, tuberculosis diagnosis in patients of varying age, ethnicity and gender, and phenotype prediction in the presence of population structure and outperforms state-of-the-art methods in terms of prediction accuracy.

Availability: A ccSVM-implementation in MATLAB is available from http://webdav.tuebingen.mpg.de/u/karsten/Forschung/ISMB11_ccSVM/.

Contact: limin.li@tuebingen.mpg.de; karsten.borgwardt@tuebingen.mpg.de.

Show MeSH
Related in: MedlinePlus