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The present study was focused on developing a computational procedure for analysis of the HPLC metabonomics fingerprints of human urine to distinguish between patients with breast cancer from healthy people. The predictive rate of support vector machine (SVM) based diagnosis model is 100% for training set and 93.2% for test set, respectively. Current work might have important reference values to explore...
This paper introduced a straightforward and effective chromatographic data pre-processing method developing for utilization prior to chemometric analysis of large metabonomic dataset arising from high performance liquid chromatography. Nucleotides chromatographic fingerprinting in human urines was employed to validate the proposed method. Performance for discrimination cancer samples from healthy...
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