ABSTRACT
Different biomarker patterns, such as those of molecular biomarkers and ratio biomarkers, have their own merits in clinical applications. In this study, a novel machine learning method used in biomedical data analysis for constructing classification models by combining different biomarker patterns (CDBP)is proposed. CDBP uses relative expression reversals to measure the discriminative ability of different biomarker patterns, and selects the pattern with the higher score for classifier construction. The decision boundary of CDBP can be characterized in simple and biologically meaningful manners. The CDBP method was compared with eight state-of-the-art methods on eight gene expression datasets to test its performance. CDBP, with fewer features or ratio features, had the highest classification performance. Subsequently, CDBP was employed to extract crucial diagnostic information from a rat hepatocarcinogenesis metabolomics dataset. The potential biomarkers selected by CDBP provided better classification of hepatocellular carcinoma (HCC)and non-HCC stages than previous works in the animal model. The statistical analyses of these potential biomarkers in an independent human dataset confirmed their discriminative abilities of different liver diseases. These experimental results highlight the potential of CDBP for biomarker identification from high-dimensional biomedical datasets and demonstrate that it can be a useful tool for disease classification.
