公共化学
代谢组学
计算机科学
鉴定(生物学)
支持向量机
Python(编程语言)
代谢物
人工智能
数据挖掘
机器学习
计算生物学
化学
生物信息学
生物
生物化学
操作系统
植物
作者
Markus Heinonen,Huibin Shen,Nicola Zamboni,Juho Rousu
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2012-07-18
卷期号:28 (18): 2333-2341
被引量:143
标识
DOI:10.1093/bioinformatics/bts437
摘要
Abstract Motivation: Metabolite identification from tandem mass spectra is an important problem in metabolomics, underpinning subsequent metabolic modelling and network analysis. Yet, currently this task requires matching the observed spectrum against a database of reference spectra originating from similar equipment and closely matching operating parameters, a condition that is rarely satisfied in public repositories. Furthermore, the computational support for identification of molecules not present in reference databases is lacking. Recent efforts in assembling large public mass spectral databases such as MassBank have opened the door for the development of a new genre of metabolite identification methods. Results: We introduce a novel framework for prediction of molecular characteristics and identification of metabolites from tandem mass spectra using machine learning with the support vector machine. Our approach is to first predict a large set of molecular properties of the unknown metabolite from salient tandem mass spectral signals, and in the second step to use the predicted properties for matching against large molecule databases, such as PubChem. We demonstrate that several molecular properties can be predicted to high accuracy and that they are useful in de novo metabolite identification, where the reference database does not contain any spectra of the same molecule. Availability: An Matlab/Python package of the FingerID tool is freely available on the web at http://www.sourceforge.net/p/fingerid. Contact: markus.heinonen@cs.helsinki.fi
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