Improved machine learning method for analysis of gas phase chemistry of peptides.
Improved machine learning method for analysis of gas phase chemistry of peptides.
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DOI：
10.1186/1471-2105-9-515
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发表时间：
2008-12-03
影响因子：
3
通讯作者：
Cios K
中科院分区：
文献类型：
--
作者：
Gehrke A;Sun S;Kurgan L;Ahn N;Resing K;Kafadar K;Cios K
准确的肽鉴定对于使用质谱的高通量蛋白质组学分析是重要的。搜索程序将来自复杂肽的碎片光谱（MS/MS）与来自蛋白质序列数据库的理论衍生光谱进行比较。通过基于模拟肽的气相化学的理论光谱实现了改进的区分，但是对这些过程的有限理解影响了从理论光谱预测的准确性。我们采用了一个强大的数据挖掘策略，使用新的功能注释功能的MAE软件，这揭示了在第二个肽键断裂的发生频率的预测不足。我们应用探索性数据分析的方法来预处理MS/MS谱中的信息，包括数据归一化和属性选择，以将属性减少到更小、相关性更低的集合，用于机器学习研究。然后，我们将我们的规则构建机器学习程序DataSqueezer与常用的关联规则和决策树算法进行了比较。所有使用的机器学习算法都产生了类似的结果，这些结果与第二肽键处的第二气相机制的预期性质一致。结果提供了令人信服的证据，我们已经确定了潜在的化学性质的数据，表明存在一个额外的气相机制的第二个肽键。因此，在这项研究中描述的方法提供了一个有价值的方法，在未来的分析。
Accurate peptide identification is important to high-throughput proteomics analyses that use mass spectrometry. Search programs compare fragmentation spectra (MS/MS) of peptides from complex digests with theoretically derived spectra from a database of protein sequences. Improved discrimination is achieved with theoretical spectra that are based on simulating gas phase chemistry of the peptides, but the limited understanding of those processes affects the accuracy of predictions from theoretical spectra. We employed a robust data mining strategy using new feature annotation functions of MAE software, which revealed under-prediction of the frequency of occurrence in fragmentation of the second peptide bond. We applied methods of exploratory data analysis to pre-process the information in the MS/MS spectra, including data normalization and attribute selection, to reduce the attributes to a smaller, less correlated set for machine learning studies. We then compared our rule building machine learning program, DataSqueezer, with commonly used association rules and decision tree algorithms. All used machine learning algorithms produced similar results that were consistent with expected properties for a second gas phase mechanism at the second peptide bond. The results provide compelling evidence that we have identified underlying chemical properties in the data that suggest the existence of an additional gas phase mechanism for the second peptide bond. Thus, the methods described in this study provide a valuable approach for analyses of this kind in the future.
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DOI：
10.1109/tsmcb.2005.852983
发表时间：
2006-02-01
影响因子：
--
作者：
Kurgan, LA;Cios, KJ;Dick, S
通讯作者：
Dick, S
影响因子：
7
作者：
Sun, Shaojun;Meyer-Arendt, Karen;Resing, Katheryn A.
通讯作者：
Resing, Katheryn A.
影响因子：
7.4
作者：
Zhang, ZQ
通讯作者：
Zhang, ZQ
DOI：
10.1016/j.jasms.2006.02.016
发表时间：
2006-06-01
影响因子：
3.2
作者：
Zhang, Zhongqi;Bordas-Nagy, Joseph
通讯作者：
Bordas-Nagy, Joseph
影响因子：
46.9
作者：
Elias, JE;Gibbons, FD;Gygi, SP
通讯作者：
Gygi, SP