政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/72228

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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/72228

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/72228

Title:	Multi-spectra CWT-based Algorithm (MCWT) in Mass Spectra for Peak Extraction
Authors:	Hsueh,Huey-Miin 薛慧敏;郭訓志;蔡政安
Contributors:	統計系
Date:	2008-10
Issue Date:	2014-12-23 15:20:00 (UTC+8)
Abstract:	An important objective in mass spectrometry (MS) is to identify a set of biomarkers that can be used to potentially distinguish patients between distinct treatments (or conditions) from tens or hundreds of spectra. A common two-step approach involving peak extraction and quantification is employed to identify the features of scientific interest. The selected features are then used for further investigation to understand underlying biological mechanism of individual protein or for development of genomic biomarkers to early diagnosis. However, the use of inadequate or ineffective peak detection and peak alignment algorithms in peak extraction step may lead to a high rate of false positives. Also, it is crucial to reduce the false positive rate in detecting biomarkers from ten or hundreds of spectra. Here a new procedure is introduced for feature extraction in mass spectrometry data that extends the continuous wavelet transform-based (CWT-based) algorithm to multiple spectra. The proposed multispectra CWT-based algorithm (MCWT) not only can perform peak detection for multiple spectra but also carry out peak alignment at the same time. The author` MCWT algorithm constructs a reference, which integrates information of multiple raw spectra, for feature extraction. The algorithm is applied to a SELDI-TOF mass spectra data set provided by CAMDA 2006 with known polypeptide m/z positions. This new approach is easy to implement and it outperforms the existing peak extraction method from the Bioconductor PROcess package.
Relation:	Journal of Biopharmaceutical Statistics,18(5),869-882
Data Type:	article
Appears in Collections:	[統計學系] 期刊論文

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