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Peak detection in mass spectrometry by Gabor filters and envelope analysis
- Source :
- Journal of bioinformatics and computational biology. 7(3)
- Publication Year :
- 2008
-
Abstract
- Mass Spectrometry (MS) is increasingly being used to discover diseases-related proteomic patterns. The peak detection step is one of the 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 discovery rate in peak detection. Most of them follow two approaches: one is the denoising approach and the other is the decomposing approach. In the previous studies, the decomposition of MS data method shows more potential than the first one. In this paper, we propose two novel methods, named GaborLocal and GaborEnvelop, both of which can detect more true peaks with a lower false discovery rate than previous methods. We employ the method of Gaussian local maxima to detect peaks, because it is robust to noise in signals. A new approach, peak rank, is defined for the first time to identify peaks instead of using the signal-to-noise ratio. Meanwhile, the Gabor filter is used to amplify important information and compress noise in the raw MS signal. Moreover, we also propose the envelope analysis to improve the quantification of peaks and remove more false peaks. The proposed methods have been performed on the real SELDI-TOF spectrum with known polypeptide positions. The experimental results demonstrate that our methods outperform other commonly used methods in the Receiver Operating Characteristic (ROC) curve.
- Subjects :
- False discovery rate
Proteomics
Speech recognition
Noise reduction
Gaussian
Biochemistry
Mass Spectrometry
symbols.namesake
Gabor filter
Software Design
Humans
Envelope (mathematics)
Molecular Biology
Mathematics
Receiver operating characteristic
Noise (signal processing)
business.industry
Computational Biology
Pattern recognition
Computer Science Applications
Maxima and minima
ROC Curve
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
symbols
Artificial intelligence
business
Peptides
Algorithms
Subjects
Details
- ISSN :
- 02197200
- Volume :
- 7
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Journal of bioinformatics and computational biology
- Accession number :
- edsair.doi.dedup.....dff86bf555197ac7dee7991912d07002