2008
DOI: 10.1186/1477-5956-6-6
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Data mining of plasma peptide chromatograms for biomarkers of air contaminant exposures

Abstract: Background: Interrogation of chromatographic data for biomarker discovery becomes a tedious task due to stochastic variability in retention times arising from solvent and column performance. The difficulty is further compounded when the effects of exposure (e.g. to environmental contaminants) and biological variability result in varying numbers and intensities of peaks among chromatograms.

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Cited by 5 publications
(2 citation statements)
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“…In our experience, 2-DE identified and revealed relative distribution of more abundant proteins whereas LC-ESI-MS/MS identified less abundant proteins and hydrophobic proteins not amenable to detection by 2-DE. After the immuno-LCM procedure, cells were lyzed and fractionated by molecular weight differences using SDS-PAGE, because SDS-PAGE was not only an effective pre-fractionation method [ 32 ], but also provided approximate molecular weight information for identified proteins [ 33 ]. One previously described immunostaining method for proteomics was based on immunogold detection and involved very short incubation times (5 min) and very high antibody titers (1/25-1/5) [ 34 ].…”
Section: Discussionmentioning
confidence: 99%
“…In our experience, 2-DE identified and revealed relative distribution of more abundant proteins whereas LC-ESI-MS/MS identified less abundant proteins and hydrophobic proteins not amenable to detection by 2-DE. After the immuno-LCM procedure, cells were lyzed and fractionated by molecular weight differences using SDS-PAGE, because SDS-PAGE was not only an effective pre-fractionation method [ 32 ], but also provided approximate molecular weight information for identified proteins [ 33 ]. One previously described immunostaining method for proteomics was based on immunogold detection and involved very short incubation times (5 min) and very high antibody titers (1/25-1/5) [ 34 ].…”
Section: Discussionmentioning
confidence: 99%
“…Karthikeyan et al [113] applied SVM and the genetic algorithm (GA) to plasma peptide chromatograms for identifying biomarkers of air contaminant exposures. Interrogation of chromatographic data for biomarker discovery is hampered by the stochastic variability in retention times; the difficulty is further increased when the effects of exposure (e.g.…”
Section: Support Vector Machines (Svm) Approachesmentioning
confidence: 99%