Integrative Analysis

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Integrative analysis has been primarily used to prioritize disease genes or chromosomal regions for experimental testing, to discover disease subtypes or to predict patient survival or other clinical variables. The ultimate goal of this work is to propose a machine learning approach which is functional in both data fusion and supervised learning. We further analyzed the potential benefits of merging microarray and clinical data sets for prognostic application in breast cancer diagnosis.
We integrate microarray and clinical data into one mathematical model, for the development of highly homogeneous classifiers in clinical decision support. For this purpose, we present a kernel based integration framework in which each data set is transformed
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To verify the merit of the proposed approach over the single data sources such as clinical and microarray data, the LS-SVM were built on all data sets individually for classifying cancer patients. Next, GEVD and kernel GEVD were used as pre-processing step. Then the data in the projected space (scores) have used to build the LS-SVM classifier. Results shows that these types of integration information helped us to achieve better prediction performances than considering single data sets. Integration of different data sources are relevant in cancer studies for better diagnosis, prognosis and personnel therapy.
In addition, the results suggest that kernel based data integration increases the predictive performance of clinical decision support models. This indicates that there might be non-linear pattern in the data that effectively modelled with kernel based techniques. Finally weighted LS-SVM approach was used for the integration of both microarray and clinical kernel functions and performed subsequent classifications. The weighted LS-SVM classifier proposes a new optimization framework to solve the problem of classification using features
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Such studies are required to determine, which data sets are most significant to be considered as weighting matrix. The proposed weighted LS-SVM classifier integrates heterogeneous data sets to achieve good performing and affordable classifiers. The results suggest that the use of our integration approach on gene expression and clinical data can improve the performance of decision making in cancer.
We proposed a weighted LS-SVM classifier for the integration of two data sources and further prediction task. Each data set is represented with a kernel matrix, based on the RBF kernel function. The proposed clinical classifier gives a step towards improving predictions for individual patients about prognosis, metastatic phenotype and therapy responses.
Because the parameters (bandwidth for kernel matrices and regularization term of weighted LS-SVM) had to be optimized, all possible combinations of these parameters were investigated with a LOO-CV. Since these parameters optimization strategy is time consuming, one can further investigate a parameter optimization criterion for kernel GEVD and weighted LS-SVM.
The applications of proposed method are not limited to clinical and

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