Identification of biomarkers from mass spectrometry data using a "common" peak approach

Background: Proteomic data obtained from mass spectrometry have attracted great interest for the detection of early-stage cancer. However, as mass spectrometry data are high-dimensional, identification of biomarkers is a key problem. Results: This paper proposes the use of "common" peaks in data as biomarkers. Analysis is conducted as follows: data preprocessing, identification of biomarkers, and application of AdaBoost to construct a classification function. Informative "common" peaks are selected by AdaBoost. AsymBoost is also examined to balance false negatives and false positives. The effectiveness of the approach is demonstrated using an ovarian cancer dataset. Conclusion: Continuous covariates and discrete covariates can be used in the present approach. The difference between the result for the continuous covariates and that for the discrete covariates was investigated in detail. In the example considered here, both covariates provide a good prediction, but i...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Tadayoshi Fushiki, Hironori Fujisawa, Shinto Eguchi
• Keywords: BMCBI 2006, Continuous Covariates, Mass Spectrometry, Proteomic Data