Large-Scale Prediction of Cationic Metabolite Identity and Migration Time in Capillary Electrophoresis Mass Spectrometry Using Artificial Neural Networks

详细信息    查看全文

• 作者：Masahiro Sugimoto ; Shinichi Kikuchi ; Masanori Arita ; Tomoyoshi Soga ; Takaaki Nishioka ; and Masaru Tomita
• 刊名：Analytical Chemistry
• 出版年：2005
• 出版时间：January 1, 2005
• 年：2005
• 卷：77
• 期：1
• 页码：78 - 84
• 全文大小：113K
• 年卷期：v.77,no.1(January 1, 2005)
• ISSN：1520-6882

文摘

We developed a computational technique to assist in thelarge-scale identification of charged metabolites. Theelectrophoretic mobility of metabolites in capillary electrophoresis-mass spectrometry (CE-MS) was predictedfrom their structure, using an ensemble of artificial neuralnetworks (ANNs). Comparison between relative migrationtimes of 241 various cations measured by CE-MS andpredicted by a trained ANN ensemble produced a correlation coefficient of 0.931. When we used our techniqueto characterize all metabolites listed in the KEGG liganddatabase, the correct compounds among the top threecandidates were predicted in 78.0% of cases. We suggestthat this approach can be used for the prediction of themigration time of any cation and that it represents apowerful method for the identification of uncharacterizedCE-MS peaks in metabolome analysis.