Process / pipelineBioinformatics / omics
贝叶斯蛋白质组学分析——基于质谱数据的概率推断
贝叶斯蛋白质组学分析将概率模型应用于质谱数据，以识别肽段、推断蛋白质存在并量化不同条件下蛋白质的差异丰度。通过编码先验知识并在流程的每个步骤中传播不确定性，贝叶斯方法产生经过校准的识别和量化后验概率，而非简单的点估计，从而能够比纯粹的频率学方法更原则性地控制错误发现率，并更真实地报告不确定性。
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来源
- Kall, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2008). Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods, 5(11), 923–925. link ↗
- Choi, H., & Nesvizhskii, A. I. (2008). Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics. Journal of Proteome Research, 7(1), 254–265. link ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Statistical Analysis of Proteomics Data. ScholarGate. https://scholargate.app/zh/bioinformatics/bayesian-proteomics-analysis
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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