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2D Quantitative Structure-Property Relationship Study of Mycotoxins by Multiple Linear Regression and Support Vector Machine.
MedLine Citation:
PMID:  20957079     Owner:  NLM     Status:  PubMed-not-MEDLINE    
Abstract/OtherAbstract:
In the present work, support vector machines (SVMs) and multiple linear regression (MLR) techniques were used for quantitative structure-property relationship (QSPR) studies of retention time (t(R)) in standardized liquid chromatography-UV-mass spectrometry of 67 mycotoxins (aflatoxins, trichothecenes, roquefortines and ochratoxins) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and genetic algorithm method of variable selection, the most relevant descriptors were selected to build QSPR models. MLR and SVMs methods were employed to build QSPR models. The robustness of the QSPR models was characterized by the statistical validation and applicability domain (AD). The prediction results from the MLR and SVM models are in good agreement with the experimental values. The correlation and predictability measure by r(2) and q(2) are 0.931 and 0.932, repectively, for SVM and 0.923 and 0.915, respectively, for MLR. The applicability domain of the model was investigated using William's plot. The effects of different descriptors on the retention times are described.
Authors:
Roya Khosrokhavar; Jahan Bakhsh Ghasemi; Fereshteh Shiri
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Publication Detail:
Type:  Journal Article     Date:  2010-08-31
Journal Detail:
Title:  International journal of molecular sciences     Volume:  11     ISSN:  1422-0067     ISO Abbreviation:  Int J Mol Sci     Publication Date:  2010  
Date Detail:
Created Date:  2010-10-19     Completed Date:  2011-07-14     Revised Date:  2013-05-29    
Medline Journal Info:
Nlm Unique ID:  101092791     Medline TA:  Int J Mol Sci     Country:  Switzerland    
Other Details:
Languages:  eng     Pagination:  3052-68     Citation Subset:  -    
Affiliation:
Food and Drug Laboratory Research Center, MOH & ME, Tehran, Iran; E-Mail: khosrokhavar_r@yahoo.com.
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