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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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Rank 48 out of 119 in category Statistics & Probability in the 2013 Thomson Reuters Journal Citation Report/Science Edition


Application of the Random Forest Classification Method to Peaks Detected from Mass Spectrometric Proteomic Profiles of Cancer Patients and Controls

Jennifer H Barrett1 / David A Cairns2

1Section of Epidemiology and Biostatistics, Leeds Institute of Molecular Medicine

2Section of Oncology and Clinical Research, Leeds Institute of Molecular Medicine

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 7, Issue 2, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1349, February 2008

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The random forest classification method was applied to classify samples from 76 breast cancer patients and 77 controls whose proteomic profile had been obtained using mass spectrometry. The analysis consisted of two stages, the detection of peaks from the profiles and the construction of a classification rule using random forests. Using a peak detection method based on finding common local maxima in the smoothed sample spectra, 444 peaks were detected, reducing to 365 robust peaks found in at least 7 out of 10 random subsets of samples. Subjects were classified as cases or controls using the random forest algorithm applied to the 365 peaks. Based on the prediction of the status of out-of-bag samples, the total error rate was 16.3%, with a sensitivity of 81.6% and a specificity of 85.7%. Measures of importance of each of the peaks were calculated to identify regions of the spectrum influencing the classification, and the four most important peaks were identified as mz3863_13, mz2943_12, mz3193_44 and mz8925_94. Combining initial peak detection with the random forest algorithm provides a high-performance classification system for proteomic data, with unbiased estimates of future performance.

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