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Volumn 904, Issue 2, 2000, Pages 119-129
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Artificial neural networks in liquid chromatography: Efficient and improved quantitative structure-retention relationship models
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Author keywords
Artificial neural networks; Quantitative structure retention relationships
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Indexed keywords
ALUMINUM OXIDE;
POLYETHYLENE;
SILICON DIOXIDE;
UNCLASSIFIED DRUG;
ACCURACY;
ALGORITHM;
ARTICLE;
ARTIFICIAL NEURAL NETWORK;
CHEMICAL STRUCTURE;
CONTROLLED STUDY;
LIQUID CHROMATOGRAPHY;
MODEL;
MULTIPLE REGRESSION;
MULTIVARIATE ANALYSIS;
PHYSICAL CHEMISTRY;
PRIORITY JOURNAL;
QUANTITATIVE STRUCTURE ACTIVITY RELATION;
RETENTION;
SOLUTE;
STATIONARY PHASE;
TECHNIQUE;
TRAINING;
ALGORITHMS;
CHROMATOGRAPHY, LIQUID;
LINEAR MODELS;
NEURAL NETWORKS (COMPUTER);
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;
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EID: 0034731664
PISSN: 00219673
EISSN: None
Source Type: Journal
DOI: 10.1016/S0021-9673(00)00923-7 Document Type: Article |
Times cited : (83)
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References (24)
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