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Predicting breast cancer using an expression values weighted clinical classifier.

Thomas M, De Brabanter K, Suykens JA, De Moor B - BMC Bioinformatics (2014)

Bottom Line: These studies often remain inconclusive regarding an obtained improvement in prediction performance.While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters.Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy.

View Article: PubMed Central - PubMed

Affiliation: KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics/iMinds Future Health Department, Kasteelpark Arenberg 10, Leuven, 3001, Belgium. minta.thomas@esat.kuleuven.be.

ABSTRACT

Background: Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters.

Results: We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies.

Conclusions: Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.

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Related in: MedlinePlus

Overview of algorithm. The data sets represented as matrices with rows corresponding to patients and columns corresponding to genes and clinical parameters respectively for first and second data sets. LOO-CV is applied to select the optimal parameters.
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Fig1: Overview of algorithm. The data sets represented as matrices with rows corresponding to patients and columns corresponding to genes and clinical parameters respectively for first and second data sets. LOO-CV is applied to select the optimal parameters.

Mentions: The proposed method is a new machine learning approach in data fusion and subsequent classifications. In this study, the advantages of a weighted LS-SVM classifier were explored, by designing a clinical classifier. This clinical classifier combined kernels by weighting kernel inner product from one data set with that from the other data set. Here we considered microarray kernels as weighting matrix for clinical kernels. In each of these case studies, we compared the prediction performance of individual data sets with GEVD, kernel GEVD and weighted LS-SVM classifier. In kernel GEVD, σ1 and σ2 are the bandwidth of RBF-kernel function of clinical and microarray data sets respectively. These parameters were chosen such that the pairs (σ1, σ2) which obtained the highest LOO-CV performance. The parameter selection (see Algorithm) for the weighted LS-SVM classifier are illustrated in Figure 1. For several possible values of the kernel parameters σ1 and σ2, the LOO cross validation performance is computed for each possible combinations of γ. The optimal parameters are the combinations (σ1, σ2, γ) with best LOO-CV performance. Remark the complexity of this optimization procedure because both the kernel parameters (σ1 and σ2) and γ need to be optimized in the sense of the LOO-CV performance.Figure 1


Predicting breast cancer using an expression values weighted clinical classifier.

Thomas M, De Brabanter K, Suykens JA, De Moor B - BMC Bioinformatics (2014)

Overview of algorithm. The data sets represented as matrices with rows corresponding to patients and columns corresponding to genes and clinical parameters respectively for first and second data sets. LOO-CV is applied to select the optimal parameters.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4308909&req=5

Fig1: Overview of algorithm. The data sets represented as matrices with rows corresponding to patients and columns corresponding to genes and clinical parameters respectively for first and second data sets. LOO-CV is applied to select the optimal parameters.
Mentions: The proposed method is a new machine learning approach in data fusion and subsequent classifications. In this study, the advantages of a weighted LS-SVM classifier were explored, by designing a clinical classifier. This clinical classifier combined kernels by weighting kernel inner product from one data set with that from the other data set. Here we considered microarray kernels as weighting matrix for clinical kernels. In each of these case studies, we compared the prediction performance of individual data sets with GEVD, kernel GEVD and weighted LS-SVM classifier. In kernel GEVD, σ1 and σ2 are the bandwidth of RBF-kernel function of clinical and microarray data sets respectively. These parameters were chosen such that the pairs (σ1, σ2) which obtained the highest LOO-CV performance. The parameter selection (see Algorithm) for the weighted LS-SVM classifier are illustrated in Figure 1. For several possible values of the kernel parameters σ1 and σ2, the LOO cross validation performance is computed for each possible combinations of γ. The optimal parameters are the combinations (σ1, σ2, γ) with best LOO-CV performance. Remark the complexity of this optimization procedure because both the kernel parameters (σ1 and σ2) and γ need to be optimized in the sense of the LOO-CV performance.Figure 1

Bottom Line: These studies often remain inconclusive regarding an obtained improvement in prediction performance.While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters.Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy.

View Article: PubMed Central - PubMed

Affiliation: KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics/iMinds Future Health Department, Kasteelpark Arenberg 10, Leuven, 3001, Belgium. minta.thomas@esat.kuleuven.be.

ABSTRACT

Background: Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters.

Results: We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies.

Conclusions: Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.

Show MeSH
Related in: MedlinePlus