Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks.
Large-scale prediction of cationic metabolite identity and migration time in capillary electrophoresis mass spectrometry using artificial neural networks.
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DOI：
10.1021/ac048950g
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发表时间：
2005
影响因子：
7.4
通讯作者：
M. Sugimoto;S. Kikuchi;Masanori Arita;T. Soga;T. Nishioka;M. Tomita
中科院分区：
文献类型：
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作者：
M. Sugimoto;S. Kikuchi;Masanori Arita;T. Soga;T. Nishioka;M. Tomita
我们开发了一种计算技术来协助大规模识别带电代谢物。使用人工神经网络 (ANN) 集合，根据代谢物的结构预测毛细管电泳-质谱 (CE-MS) 中代谢物的电泳迁移率。通过 CE-MS 测量和经过训练的 ANN 系综预测的 241 种不同阳离子的相对迁移时间之间的比较产生了 0.931 的相关系数。当我们使用我们的技术来表征 KEGG 配体数据库中列出的所有代谢物时，在 78.0% 的情况下预测了前三种候选物中的正确化合物。我们建议该方法可用于预测任何阳离子的迁移时间，并且它代表了一种在代谢组分析中识别未表征的 CE-MS 峰的强大方法。
We developed a computational technique to assist in the large-scale identification of charged metabolites. The electrophoretic mobility of metabolites in capillary electrophoresis-mass spectrometry (CE-MS) was predicted from their structure, using an ensemble of artificial neural networks (ANNs). Comparison between relative migration times of 241 various cations measured by CE-MS and predicted by a trained ANN ensemble produced a correlation coefficient of 0.931. When we used our technique to characterize all metabolites listed in the KEGG ligand database, the correct compounds among the top three candidates were predicted in 78.0% of cases. We suggest that this approach can be used for the prediction of the migration time of any cation and that it represents a powerful method for the identification of uncharacterized CE-MS peaks in metabolome analysis.