Gas chromatographic quantitative structure–retention relationships of trimethylsilylated anabolic androgenic steroids by multiple linear regression and partial least squares

Least Squares ◽

Partial Least Squares ◽

Anabolic Androgenic Steroids ◽

Quantitative Structure ◽

Structure Retention ◽

Androgenic Steroids ◽

Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids

Journal of Chromatography A ◽

10.1016/j.chroma.2012.07.064 ◽

2012 ◽

Vol 1256 ◽

pp. 232-239 ◽

Cited By ~ 20

Author(s):

A.G. Fragkaki ◽

E. Farmaki ◽

N. Thomaidis ◽

A. Tsantili-Kakoulidou ◽

Y.S. Angelis ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Linear Regression ◽

Least Squares ◽

Partial Least Squares ◽

Anabolic Androgenic Steroids ◽

Relative Retention ◽

Artificial Neural ◽

Androgenic Steroids

Quantitative structure–retention relationship study of α-, β1-, and β2-agonists using multiple linear regression and partial least-squares procedures

Analytica Chimica Acta ◽

10.1016/j.aca.2004.02.019 ◽

2004 ◽

Vol 512 (1) ◽

pp. 165-171 ◽

Cited By ~ 20

Author(s):

A.G Fragkaki ◽

M.A Koupparis ◽

C.G Georgakopoulos

Keyword(s):

Linear Regression ◽

Quantitative Structure Retention Relationship

Least Squares ◽

Partial Least Squares ◽

Quantitative Structure ◽

Structure Retention ◽

Relationship Study ◽

Β2 Agonists ◽

Comparison of quantum mechanics protocols during the evaluation of Quantitative Structure-Retention Relationships supported by Genetic-Algorithm Multiple Linear Regression

Journal of Chromatography Open ◽

10.1016/j.jcoa.2021.100019 ◽

2021 ◽

pp. 100019

Author(s):

Szymon Ulenberg ◽

Tomasz Bączek

Keyword(s):

Genetic Algorithm ◽

Quantum Mechanics ◽

Linear Regression ◽

Quantitative Structure ◽

Structure Retention ◽

Comparison of multiple linear regression, partial least squares and artificial neural network for quantitative structure retention relationships of some polycyclic aromatic hydrocarbons

Analytical Methods ◽

10.1039/c2ay25711d ◽

2012 ◽

Vol 4 (10) ◽

pp. 3381 ◽

Cited By ~ 6

Author(s):

Mohamad Mahani ◽

Homa ShaikhGhomi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Polycyclic Aromatic Hydrocarbons ◽

Linear Regression ◽

Least Squares ◽

Aromatic Hydrocarbons ◽

Quantitative Structure ◽

Polycyclic Aromatic ◽

Structure Retention

Quantitative Structure-Retention Relationships Studies of Selected Groups of Compounds Characterized by Different Pharmacological Activity Using Multiple Linear Regression Procedure

Letters in Drug Design & Discovery ◽

10.2174/1570180811666140506204742 ◽

2014 ◽

Vol 11 (8) ◽

pp. 1017-1039

Author(s):

Jolanta Stasiak ◽

Marcin Koba ◽

Tomasz Baczek

Keyword(s):

Linear Regression ◽

Pharmacological Activity ◽

Quantitative Structure ◽

Regression Procedure ◽

Structure Retention ◽

Quantitative structure–property relationship study of acidity constants of some 9,10-anthraquinone derivatives using multiple linear regression and partial least-squares procedures

Talanta ◽

10.1016/s0039-9140(01)00374-5 ◽

2001 ◽

Vol 54 (6) ◽

pp. 1113-1120 ◽

Cited By ~ 39

Author(s):

M Shamsipur

Keyword(s):

Linear Regression ◽

Least Squares ◽

Partial Least Squares ◽

Quantitative Structure ◽

Structure Property ◽

Acidity Constants ◽

Structure Property Relationship ◽

Anthraquinone Derivatives ◽

Relationship Study

High-performance liquid chromatography of chalcones: Quantitative structure-retention relationships using partial least-squares (PLS) modeling

Chromatographia ◽

10.1007/bf02505412 ◽

2000 ◽

Vol 51 (11-12) ◽

pp. 727-735 ◽

Cited By ~ 21

Author(s):

M. P. Montaña ◽

N. B. Pappano ◽

N. B. Debattista ◽

J. Raba ◽

J. M. Luco

Keyword(s):

High Performance Liquid Chromatography ◽

Liquid Chromatography ◽

Least Squares ◽

Partial Least Squares ◽

High Performance ◽

Quantitative Structure ◽

Pls Modeling ◽

Structure Retention ◽

Combinatorial protocol in multiple linear regression/partial least-squares directed rationale for the caspase-3 inhibition activity of isoquinoline-1,3,4-trione derivatives

SAR and QSAR in Environmental Research ◽

10.1080/10629360903570545 ◽

2010 ◽

Vol 21 (1-2) ◽

pp. 169-185 ◽

Cited By ~ 4

Author(s):

B.K. Sharma ◽

P. Pilania ◽

P. Singh ◽

Y.S. Prabhakar

Keyword(s):

Linear Regression ◽

Least Squares ◽

Partial Least Squares ◽

Caspase 3 ◽

Inhibition Activity

Prediction of retention indices for frequently reported compounds of plant essential oils using multiple linear regression, partial least squares, and support vector machine

Journal of Separation Science ◽

10.1002/jssc.201300254 ◽

2013 ◽

Vol 36 (15) ◽

pp. 2464-2471 ◽

Cited By ~ 10

Author(s):

Jun Yan ◽

Jian-Hua Huang ◽

Min He ◽

Hong-Bing Lu ◽

Rui Yang ◽

...

Keyword(s):

Support Vector Machine ◽

Linear Regression ◽

Essential Oils ◽

Least Squares ◽

Partial Least Squares ◽

Retention Indices ◽

Support Vector ◽

Plant Essential Oils ◽

Prediction Of Retention

QSAR study of the non-peptidic inhibitors of procollagen C-proteinase based on Multiple linear regression, principle component regression, and partial least squares

Arabian Journal of Chemistry ◽

10.1016/j.arabjc.2015.02.016 ◽

2017 ◽

Vol 10 (6) ◽

pp. 801-810 ◽

Cited By ~ 3

Author(s):

Ardeshir Khazaei ◽

Negin Sarmasti ◽

Jaber Yousefi Seyf ◽

Zahra Rostami ◽

Mohammad Ali Zolfigol

Keyword(s):

Linear Regression ◽

Least Squares ◽

Partial Least Squares ◽

Qsar Study ◽

Principle Component ◽

Principle Component Regression ◽

Peptidic Inhibitors