Zhou Bin, Cheema Amrita K, Ressom Habtom W
Department of Electrical and Computer Engineering at Virginia Polytechnic Institute and State University, Falls Church, VA 22043 USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:756-9. doi: 10.1109/IEMBS.2010.5626337.
Mass spectrometry-based metabolomics is getting mature and playing an ever important role in the systematic understanding of biological process in conjunction with other members of "-omics" family. However, the identification of metabolites in untargeted metabolomics profiling remains a challenge. In this paper, we propose a support vector machine (SVM)-based spectral matching algorithm to combine multiple similarity measures for accurate identification of metabolites. We compared the performance of this approach with several existing spectral matching algorithms on a spectral library we constructed. The results demonstrate that our proposed method is very promising in identifying metabolites in the face of data heterogeneity caused by different experimental parameters and platforms.
基于质谱的代谢组学正日益成熟，并在与“组学”家族的其他成员共同系统理解生物过程中发挥着越来越重要的作用。然而，在非靶向代谢组学分析中鉴定代谢物仍然是一项挑战。在本文中，我们提出了一种基于支持向量机（SVM）的光谱匹配算法，以结合多种相似性度量来准确鉴定代谢物。我们在自己构建的光谱库上，将该方法的性能与几种现有的光谱匹配算法进行了比较。结果表明，面对由不同实验参数和平台导致的数据异质性，我们提出的方法在鉴定代谢物方面非常有前景。
