Current Mathematical Methods Used in QSAR/QSPR Studies
Created by W.Langdon from
gp-bibliography.bib Revision:1.8120
- @Article{Liu:2009:IJMS,
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title = "Current Mathematical Methods Used in {QSAR}/{QSPR}
Studies",
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author = "Peixun Liu and Wei Long",
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journal = "International Journal of Molecular Sciences",
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publisher = "Molecular Diversity Preservation International",
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year = "2009",
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ISSN = "1422-0067; 14220067",
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bibsource = "OAI-PMH server at www.doaj.org",
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language = "eng",
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oai = "oai:doaj-articles:a634e99c5db7a3846db7d582ee717285",
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keywords = "genetic algorithms, genetic programming, QSAR, QSPR,
Mathematical methods, Regression, Algorithm",
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URL = "http://www.mdpi.com/1422-0067/10/5/1978/pdf",
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DOI = "doi:10.3390/ijms10051978",
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URL = "http://www.mdpi.com/1422-0067/10/5/1978/",
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broken = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=14220067\&date=2009\&volume=10\&issue=5\&spage=1978",
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size = "21 pages",
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abstract = "This paper gives an overview of the mathematical
methods currently used in quantitative
structure-activity/property relationship (QASR/QSPR)
studies. Recently, the mathematical methods applied to
the regression of QASR/QSPR models are developing very
fast, and new methods, such as Gene Expression
Programming (GEP), Project Pursuit Regression (PPR) and
Local Lazy Regression (LLR) have appeared on the
QASR/QSPR stage. At the same time, the earlier methods,
including Multiple Linear Regression (MLR), Partial
Least Squares (PLS), Neural Networks (NN), Support
Vector Machine (SVM) and so on, are being upgraded to
improve their performance in QASR/QSPR studies. These
new and upgraded methods and algorithms are described
in detail, and their advantages and disadvantages are
evaluated and discussed, to show their application
potential in QASR/QSPR studies in the future.",
- }
Genetic Programming entries for
Peixun Liu
Wei Long
Citations