Mass Spectrometry (MS) is increasingly being used to discover disease related proteomic patterns. The peak detection step is one of most important steps in the typical analysis of MS data. Recently, many new algorithms have been proposed to increase true position rate with low false position rate in peak detection. Most of them follow two approaches: one is denoising approach and the other one is decomposing approach. In the previous studies, the decomposition of MS data method shows more potential than the first one. In this paper, we propose a new method named GaborLocal which can detect more true peaks with a very low false position rate. The Gaussian local maxima is employed for peak detection, because it is robust to noise in signals. Moreover, the maximum rank of peaks is defined at the first time to identify peaks instead of using the signal-to-noise ratio and the Gabor filter is used to decompose the raw MS signal. We perform the proposed method on the real SELDI-TOF spectrum with known polypeptide positions. The experimental results demonstrate our method outperforms other common used methods in the receiver operating characteristic (ROC) curve.
|Original language||English (US)|
|Number of pages||12|
|Journal||Computational systems bioinformatics / Life Sciences Society. Computational Systems Bioinformatics Conference|
|State||Published - 2008|
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