poster

Prediction of retention times for a large set of pesticides based on improved gene expression programming

Authors:

Kejun Zhang,

Shouqian Sun,

Hongzong SiAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 1725 - 1726

https://doi.org/10.1145/1389095.1389429

Published: 12 July 2008 Publication History

Get Access

Abstract

The purpose of the paper is to present a novel way to building Quantitative structure-retention relationship (QSRR) models. Studies was reported for predicting the retention times (RTs) of 110 pesticides which were detected by gas chromatography (GC) with mass selective detector (MSD). Chemical descriptors were calculated from the molecular structure of pesticides and the QSRR models of RTs with descriptors was built using the heuristic method (HM) and Improved Gene Expression Programming (IGEP), respectively. The obtained linear model of HM had a correlation coefficient R² = 0.913, with a root mean square error (RMS) S² of 0.0387 for the training set, while R2 =0.907, and RMS =0.0408 for the test set. The nonlinear model by IGEP gave better results: for the training set R² = 0.971, S² = 0.0176 and for the test set R² =0.951, S² =0.0267. The prediction results from nonlinear model are in agreement with the experimental values The QSRR model also reveals that the gas chromatographic RTs are associated with physicochemical property of pesticides.

References

[1]

Analytical methods for Pesticide residues in foodstuff, 6thed., General Inspectorate for Health Protect, Ministry of Health, Welfare and Spot, Amsterdam, The Netherlands,1996. 1

Google Scholar

[2]

Ock Kyoung Chun,hee Gon Kang, Estimation of risks of pesticide exposure, by food intake, to Koreans. Food and chemical Toxicology 41:1063--1076(2003). 2

Google Scholar

[3]

Katritzky, A.R.; Lobanov, V.S.; Karelson, M. Reference Manual, Version 2.0, 1994.

Google Scholar

[4]

Ferreira C, Gene expression programming: A new adaptive algorithm for solving problems., Complex Systems, 13(2001) 87--129.

Google Scholar

[5]

J.H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MIT, 1975

Digital Library

Google Scholar

[6]

D.E. Goldberg, Genetic algorithms in search, optimization, and machine learning, Addison-Wesley, Reading, MA, 1989

Digital Library

Google Scholar

[7]

Koza J R. Genetic Programming: On the Programming of computers by Means of Natural Selection. Cambridge, MA:MIT press,1992

Digital Library

Google Scholar

[8]

Ferreira, C., 2002. Gene Expression Programming in Problem Solving. in Roy, R., S. Ovaska, T. Furuhashi, and F. Hoffman, Eds., Soft Computing and Industry-Recent Applications, Springer-Verlag, pp. 635--654.

Google Scholar

[9]

M. Mitchell, An Introduction to Genetic Algorithms, in Complex Adaptive Systems. (MIT Press, 1996).(OK)

Digital Library

Google Scholar

[10]

Dewar, M.J.S., Zoebisch, E.G., Healy, E.F., Stewart, J.J.P., Development and use of quantum molecular models. 75. Comparative tests of theoretical procedures for studying chemical reactions. J. Am. Chem. Soc. 107: 3898--3902(1985).

Crossref

Google Scholar

[11]

Stewart, J.P.P., MOPAC 6.0, Quantum Chemistry Program Exchange, QCPE, No. 455, Indiana University, Bloomington, IN. 1989.

Google Scholar

[12]

Katritzky, A.R., Lobanov, V.S., Karelson, M., Comprehensive descriptors for structural and statistical analysis, Reference Manual, Version 2.0. 1994.

Google Scholar

[13]

Katritzky, A.R., Lobanov, V.S., Karelson, M.,.QSPR: the correlation and quantitative prediction of chemical and physical properties from structure. Chem. Soc. Rev. 24:279--287(1995).

Crossref

Google Scholar

[14]

Zhang Kejun, Study of Improved Gene Expression Programming for Solving Inverse problem, in 2006 Dissertation of Jiangxi University of Science and Technology, Chap. 4

Google Scholar

Cited By

View all

Browne Ndos Santos M(2010)Adaptive representations for improving evolvability, parameter control, and parallelization of gene expression programmingApplied Computational Intelligence and Soft Computing10.1155/2010/4090452010(1-19)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1155/2010/409045

Index Terms

Prediction of retention times for a large set of pesticides based on improved gene expression programming
1. Applied computing
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems

Recommendations

Automated retention time prediction of new psychoactive substances in gas chromatography
Abstract
New psychoactive substances (NPSs) are drugs that exhibit chemical structures and pharmacological actions similar to illegal drugs. Because the health hazards caused by these drugs of abuse have become a social problem, the quick identification of ...
Theoretical analysis on retention behavior of pigments in reversed-phase high-performance liquid chromatographic (HPLC)

Quantitative structure-retention relationship (QSRR) models have been used successfully to predict and explain retention behavior of pigments in reversed-phase high-performance liquid chromatography (HPLC). The semi-empirical quantum chemical method (...
Prediction of glycan structures from gene expression data based on glycosyltransferase reactions

Motivation: Glycan chains are synthesized by a combination of several kinds of glycosyltransferases (GTs). Thus, once we know the repertoire of GTs in the genome, in the transcriptome or in the proteome, it should in principle be possible to predict ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

GECCO08

Sponsor:

GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
150
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Browne Ndos Santos M(2010)Adaptive representations for improving evolvability, parameter control, and parallelization of gene expression programmingApplied Computational Intelligence and Soft Computing10.1155/2010/4090452010(1-19)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.1155/2010/409045

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Automated retention time prediction of new psychoactive substances in gas chromatography

Theoretical analysis on retention behavior of pigments in reversed-phase high-performance liquid chromatographic (HPLC)

Prediction of glycan structures from gene expression data based on glycosyltransferase reactions