On the analysis of glycomics mass spectrometry data via the regularized area under the ROC curve |
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Authors: | Jingjing Ye Hao Liu Crystal Kirmiz Carlito B Lebrilla David M Rocke |
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Institution: | (1) Department of Statistics, University of California, Davis, Davis, CA 95616, USA;(2) Division of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA;(3) Department of Chemistry, University of California, Davis, Davis, CA 95616, USA;(4) Division of Biostatistics, University of California, Davis, Davis, CA 95616, USA |
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Abstract: | Background Novel molecular and statistical methods are in rising demand for disease diagnosis and prognosis with the help of recent advanced
biotechnology. High-resolution mass spectrometry (MS) is one of those biotechnologies that are highly promising to improve
health outcome. Previous literatures have identified some proteomics biomarkers that can distinguish healthy patients from
cancer patients using MS data. In this paper, an MS study is demonstrated which uses glycomics to identify ovarian cancer.
Glycomics is the study of glycans and glycoproteins. The glycans on the proteins may deviate between a cancer cell and a normal
cell and may be visible in the blood. High-resolution MS has been applied to measure relative abundances of potential glycan
biomarkers in human serum. Multiple potential glycan biomarkers are measured in MS spectra. With the objection of maximizing
the empirical area under the ROC curve (AUC), an analysis method was considered which combines potential glycan biomarkers
for the diagnosis of cancer. |
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