Gradient boosting model for unbalanced quantitative mass spectra quality assessment

机译：用于不平衡定量质谱质量评估的梯度提升模型

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摘要

A method for controlling the quality of isotope labeled mass spectra is described here. In such mass spectra, the profiles of labeled (heavy) and unlabeled (light) peptide pairs provide us valuable information about the studied biological samples in different conditions. The core task of quality control in quantitative LC-MS experiment is to filter out low quality spectra or the peptides with error profiles. The most common used method for this problem is training a classifier for the spectra data to separate it into positive (high quality) and negative (low quality) ones. However, the small number of error profiles always makes the training data dominated by the positive samples, i.e., class imbalance problem. So the Syntheic minority over-sampling technique (SMOTE) is employed to handle the unbalanced data and then applied extreme gradient boosting (Xgboost) model as the classifier. We assessed the different heavy-light peptide ratio samples by the trained Xgboost classifier, and found that the SMOTE Xgboost classifier increases the reliability of peptide ratio estimations significantly.

机译：本文介绍了一种控制同位素标记质谱图质量的方法。在这样的质谱图中，标记（重）和未标记（轻）肽对的谱为我们提供了有关在不同条件下研究的生物样品的有价值的信息。定量LC-MS实验中质量控制的核心任务是过滤出低质量的质谱图或具有错误特征的肽段。解决此问题最常用的方法是训练光谱数据的分类器，将其分为正（高质量）和负（低质量）数据。然而，少量的误差分布图总是使训练数据被正样本支配，即类别不平衡问题。因此，采用少数综合过采样技术（SMOTE）处理不平衡数据，然后应用极限梯度增强（Xgboost）模型作为分类器。我们通过训练有素的Xgboost分类器评估了不同的重-轻肽比率样品，发现SMOTE Xgboost分类器显着提高了肽比率估计的可靠性。

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来源
《2017 International Conference on Security, Pattern Analysis, and Cybernetics》|2017年|394-399|共6页
会议地点 Shenzhen(CN)
作者
Long Chen; Tong Zhang; Tianjun Li;
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作者单位

Department of Computer and Information Science, University of Macau, Taipa, Macau, China;

School of Electronic and Information Engineering, South China University of Technology, China;

Department of Computer and Information Science, University of Macau, Taipa, Macau, China;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Peptides; Mathematical model; Training; Boosting; Isotopes; Feature extraction; Signal to noise ratio;

机译：肽;数学模型;训练;提升;同位素;特征提取;信噪比;;

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Gradient boosting model for unbalanced quantitative mass spectra quality assessment

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