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Volumn 598, Issue 1, 2007, Pages 41-50

Retention prediction of adrenoreceptor agonists and antagonists on a diol column in hydrophilic interaction chromatography

Author keywords

Adrenoreceptors agonists antagonists; Artificial neural network; Hydrophilic interaction chromatography; Mulitiple linear regression; Quantitative structure retention relationships

Indexed keywords

HYDROPHILICITY; MATHEMATICAL MODELS; NEURAL NETWORKS; PH EFFECTS;

EID: 34547771598     PISSN: 00032670     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aca.2007.07.039     Document Type: Article
Times cited : (49)

References (44)
  • 19
    • 85069413851 scopus 로고    scopus 로고
    • D.M. Diehl, E.S. Grumbach, J.R. Mazzeo, U.D. Neue, Paper No. 800-5, Pittcon 2003, Orlando, FL.
  • 40
    • 85069403971 scopus 로고    scopus 로고
    • S. Lawrence, G.L. Giles, A.C. Tsoi, What Size Neural Networks Gives Optimal Generalization? Convergence Properties of Backpropagation. Technical report UMIACS-TR-96-22 and CS-TR-3617, Institute for Advanced Computer Studies, University Maryland, College Park, MD, 1996.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.