學術產出-Periodical Articles

Article View/Open

Publication Export

Google ScholarTM

政大圖書館

Citation Infomation

  • No doi shows Citation Infomation
題名 Multi-spectra CWT-based Algorithm (MCWT) in Mass Spectra for Peak Extraction
作者 Hsueh,Huey-Miin
薛慧敏;郭訓志;蔡政安
貢獻者 統計系
日期 2008-10
上傳時間 23-Dec-2014 15:20:00 (UTC+8)
摘要 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.
關聯 Journal of Biopharmaceutical Statistics,18(5),869-882
資料類型 article
dc.contributor 統計系en_US
dc.creator (作者) Hsueh,Huey-Miinen_US
dc.creator (作者) 薛慧敏;郭訓志;蔡政安zh_TW
dc.date (日期) 2008-10en_US
dc.date.accessioned 23-Dec-2014 15:20:00 (UTC+8)-
dc.date.available 23-Dec-2014 15:20:00 (UTC+8)-
dc.date.issued (上傳時間) 23-Dec-2014 15:20:00 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/72228-
dc.description.abstract (摘要) 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.en_US
dc.format.extent 206 bytes-
dc.format.mimetype text/html-
dc.language.iso en_US-
dc.relation (關聯) Journal of Biopharmaceutical Statistics,18(5),869-882en_US
dc.title (題名) Multi-spectra CWT-based Algorithm (MCWT) in Mass Spectra for Peak Extractionen_US
dc.type (資料類型) articleen