@article{
 title = {Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection.},
 type = {article},
 year = {2010},
 identifiers = {[object Object]},
 keywords = {Chromatography, Liquid,Chromatography, Liquid: methods,Databases, Factual,Mass Spectrometry,Mass Spectrometry: methods,Models, Statistical},
 pages = {559},
 volume = {11},
 websites = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2993707&tool=pmcentrez&rendertype=abstract},
 month = {1},
 id = {aacfdf49-ec75-34cb-a525-17b169bbaf22},
 created = {2014-06-01T21:18:00.000Z},
 accessed = {2014-06-01},
 file_attached = {true},
 profile_id = {9edae5ec-3a23-3830-8934-2c27bef6ccbe},
 group_id = {63e349d6-2c70-3938-9e67-2f6483f6cbab},
 last_modified = {2014-11-19T06:03:33.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 abstract = {BACKGROUND: Liquid chromatography-mass spectrometry (LC-MS) is one of the major techniques for the quantification of metabolites in complex biological samples. Peak modeling is one of the key components in LC-MS data pre-processing.

RESULTS: To quantify asymmetric peaks with high noise level, we developed an estimation procedure using the bi-Gaussian function. In addition, to accurately quantify partially overlapping peaks, we developed a deconvolution method using the bi-Gaussian mixture model combined with statistical model selection.

CONCLUSIONS: Using extensive simulations and real data, we demonstrated the advantage of the bi-Gaussian mixture model over the Gaussian mixture model and the method of kernel smoothing combined with signal summation in peak quantification and deconvolution. The method is implemented in the R package apLCMS: http://www.sph.emory.edu/apLCMS/.},
 bibtype = {article},
 author = {Yu, Tianwei and Peng, Hesen},
 journal = {BMC bioinformatics},
 number = {1}
}

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Peak modeling is one of the key components in LC-MS data pre-processing.\n\nRESULTS: To quantify asymmetric peaks with high noise level, we developed an estimation procedure using the bi-Gaussian function. In addition, to accurately quantify partially overlapping peaks, we developed a deconvolution method using the bi-Gaussian mixture model combined with statistical model selection.\n\nCONCLUSIONS: Using extensive simulations and real data, we demonstrated the advantage of the bi-Gaussian mixture model over the Gaussian mixture model and the method of kernel smoothing combined with signal summation in peak quantification and deconvolution. The method is implemented in the R package apLCMS: http://www.sph.emory.edu/apLCMS/.","bibtype":"article","author":"Yu, Tianwei and Peng, Hesen","journal":"BMC bioinformatics","number":"1","bibtex":"@article{\n title = {Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection.},\n type = {article},\n year = {2010},\n identifiers = {[object Object]},\n keywords = {Chromatography, Liquid,Chromatography, Liquid: methods,Databases, Factual,Mass Spectrometry,Mass Spectrometry: methods,Models, Statistical},\n pages = {559},\n volume = {11},\n websites = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2993707&tool=pmcentrez&rendertype=abstract},\n month = {1},\n id = {aacfdf49-ec75-34cb-a525-17b169bbaf22},\n created = {2014-06-01T21:18:00.000Z},\n accessed = {2014-06-01},\n file_attached = {true},\n profile_id = {9edae5ec-3a23-3830-8934-2c27bef6ccbe},\n group_id = {63e349d6-2c70-3938-9e67-2f6483f6cbab},\n last_modified = {2014-11-19T06:03:33.000Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n abstract = {BACKGROUND: Liquid chromatography-mass spectrometry (LC-MS) is one of the major techniques for the quantification of metabolites in complex biological samples. 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