Machine Learning
A wavelet-based data pre-processing analysis approach in mass spectrometry
Computers in Biology and Medicine
Profiling of Mass Spectrometry Data for Ovarian Cancer Detection Using Negative Correlation Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
Information Sciences: an International Journal
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Mass Spectrometry (MS) has been applied to the early detection of ovarian cancer. To date, most of the studies concentrated on the so-called whole-spectrum approach, which treats each point in the spectrum as a separate test, due to its better accuracy than the profiling approach. However, the whole-spectrum approach does not guarantee biologically meaningful results and is difficult for biological interpretation and clinical application. Therefore, to develop an accurate profiling technique for early detection of ovarian cancer is required. This paper proposes a novel profiling method for high-resolution ovarian cancer MS data by integrating the Smoothed Nonlinear Energy Operator (SNEO), correlation-based peak selection and Random Forest classifier. In order to evaluate the performance of this novel method without bias, we employed randomization techniques by dividing the data set into testing set and training set to test the whole procedure for many times over. Test results show that the method can find a parsimonious set of biologically meaningful biomarkers with better accuracy than other methods.