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  • Title: Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index.
    Author: Mihaleva VV, Verhoeven HA, de Vos RC, Hall RD, van Ham RC.
    Journal: Bioinformatics; 2009 Mar 15; 25(6):787-94. PubMed ID: 19176550.
    Abstract:
    MOTIVATION: Matching both the retention index (RI) and the mass spectrum of an unknown compound against a mass spectral reference library provides strong evidence for a correct identification of that compound. Data on retention indices are, however, available for only a small fraction of the compounds in such libraries. We propose a quantitative structure-RI model that enables the ranking and filtering of putative identifications of compounds for which the predicted RI falls outside a predefined window. RESULTS: We constructed multiple linear regression and support vector regression (SVR) models using a set of descriptors obtained with a genetic algorithm as variable selection method. The SVR model is a significant improvement over previous models built for structurally diverse compounds as it covers a large range (360-4100) of RI values and gives better prediction of isomer compounds. The hit list reduction varied from 41% to 60% and depended on the size of the original hit list. Large hit lists were reduced to a greater extend compared with small hit lists. AVAILABILITY: http://appliedbioinformatics.wur.nl/GC-MS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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