Principal component regression analysis applied in structure-activity relationships. 2. flexible opioids with unusually high safety margin

Peter P. Mager

doi:10.1002/bimj.4710220609

Principal component regression analysis applied in structure-activity relationships. 2. flexible opioids with unusually high safety margin

Biometrical Journal ◽

10.1002/bimj.4710220609 ◽

1980 ◽

Vol 22 (6) ◽

pp. 535-543 ◽

Cited By ~ 1

Author(s):

Peter P. Mager

Keyword(s):

Regression Analysis ◽

Safety Margin ◽

Principal Component Regression ◽

Principal Component ◽

Structure Activity Relationships ◽

Structure Activity ◽

Principal Component Regression Analysis

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Principal component regression analysis applied in structure-activity relationships. 1. selective dihydrofolic acid reductase inhibitors

Biometrical Journal ◽

10.1002/bimj.4710220508 ◽

1980 ◽

Vol 22 (5) ◽

pp. 441-446 ◽

Cited By ~ 7

Author(s):

Peter P. Mager

Keyword(s):

Regression Analysis ◽

Principal Component Regression ◽

Principal Component ◽

Structure Activity Relationships ◽

Reductase Inhibitors ◽

Structure Activity ◽

Principal Component Regression Analysis

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Principal Component Regression Analysis of National Net Income and Labor Time of Occupations

MARXISM 21 ◽

10.26587/marx.14.4.201711.011 ◽

2017 ◽

Vol 14 (4) ◽

pp. 284-310

Author(s):

김덕수

Keyword(s):

Regression Analysis ◽

Principal Component Regression ◽

Principal Component ◽

Net Income ◽

Labor Time ◽

Principal Component Regression Analysis

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An Application of Spatial Population Partitioning Using Voronoi Diagrams: Principal Component Regression Analysis of Slot Machine Performance Data

Journal of Travel & Tourism Marketing ◽

10.1080/10548400902976588 ◽

2009 ◽

Vol 26 (4) ◽

pp. 415-429 ◽

Cited By ~ 2

Author(s):

Anthony Lucas ◽

Ashok K. Singh ◽

Laxmi P. Gewali ◽

Anita Singh

Keyword(s):

Regression Analysis ◽

Voronoi Diagrams ◽

Principal Component Regression ◽

Principal Component ◽

Slot Machine ◽

Performance Data ◽

Machine Performance ◽

Spatial Population ◽

Principal Component Regression Analysis

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Quantitative infrared spectrum detection of SF6 decomposition components based on principal component regression analysis

2017 China International Electrical and Energy Conference (CIEEC) ◽

10.1109/cieec.2017.8388552 ◽

2017 ◽

Author(s):

Yang Yuling ◽

Guo Xinliang ◽

Cui Zhaolun ◽

Xia Huanhuan ◽

Yang Xueying ◽

...

Keyword(s):

Regression Analysis ◽

Infrared Spectrum ◽

Principal Component Regression ◽

Principal Component ◽

Spectrum Detection ◽

Principal Component Regression Analysis ◽

Sf6 Decomposition

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Patient phenotypes associated with outcome following surgery for mild degenerative cervical myelopathy: a principal component regression analysis

The Spine Journal ◽

10.1016/j.spinee.2018.05.009 ◽

2018 ◽

Vol 18 (12) ◽

pp. 2220-2231 ◽

Cited By ~ 9

Author(s):

Jetan H. Badhiwala ◽

Christopher D. Witiw ◽

Farshad Nassiri ◽

Blessing N.R. Jaja ◽

Muhammad A. Akbar ◽

...

Keyword(s):

Regression Analysis ◽

Cervical Myelopathy ◽

Principal Component Regression ◽

Principal Component ◽

Principal Component Regression Analysis ◽

Degenerative Cervical Myelopathy

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Principal Component Regression Analysis of the Relation Between CIELAB Color and Monomeric Anthocyanins in Young Cabernet Sauvignon Wines

Molecules ◽

10.3390/molecules13112859 ◽

2008 ◽

Vol 13 (11) ◽

pp. 2859-2870 ◽

Cited By ~ 33

Author(s):

Fu-Liang Han ◽

Wen-Na Zhang ◽

Qiu-Hong Pan ◽

Cheng-Rong Zheng ◽

Hai-Yan Chen ◽

...

Keyword(s):

Regression Analysis ◽

Principal Component Regression ◽

Principal Component ◽

Cabernet Sauvignon ◽

Principal Component Regression Analysis ◽

Monomeric Anthocyanins ◽

Cielab Color

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Prediction equations for Warner–Bratzler shear force using principal component regression analysis in Brahman-influenced Venezuelan cattle

Meat Science ◽

10.1016/j.meatsci.2012.11.026 ◽

2013 ◽

Vol 93 (3) ◽

pp. 771-775 ◽

Cited By ~ 2

Author(s):

N. Jerez-Timaure ◽

N. Huerta-Leidenz ◽

J. Ortega ◽

A. Rodas-González

Keyword(s):

Regression Analysis ◽

Shear Force ◽

Principal Component Regression ◽

Principal Component ◽

Prediction Equations ◽

Principal Component Regression Analysis

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Application of principal component analysis to the study of quantitative structure-activity relationships by means of multiple regression analysis.

Chemical and Pharmaceutical Bulletin ◽

10.1248/cpb.33.1488 ◽

1985 ◽

Vol 33 (4) ◽

pp. 1488-1495 ◽

Cited By ~ 8

Author(s):

TANEKAZU KUBOTA ◽

JIRO HANAMURA ◽

KENJI KANO ◽

BUNJI UNO

Keyword(s):

Principal Component Analysis ◽

Regression Analysis ◽

Multiple Regression Analysis ◽

Multiple Regression ◽

Principal Component ◽

Component Analysis ◽

Quantitative Structure ◽

Structure Activity Relationships ◽

Structure Activity ◽

Quantitative Structure Activity Relationships

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Driving force analysis of irrigation water consumption using principal component regression analysis

Agricultural Water Management ◽

10.1016/j.agwat.2020.106089 ◽

2020 ◽

Vol 234 ◽

pp. 106089 ◽

Cited By ~ 2

Author(s):

Mengting Chen ◽

Yufeng Luo ◽

Yingying Shen ◽

Zhenzhong Han ◽

Yuanlai Cui

Keyword(s):

Regression Analysis ◽

Water Consumption ◽

Irrigation Water ◽

Driving Force ◽

Principal Component Regression ◽

Principal Component ◽

Force Analysis ◽

Principal Component Regression Analysis ◽

Driving Force Analysis

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Comparative particle size determination of phenacetin bulk powder by using Kubelka–Munk theory and principal component regression analysis based on near-infrared spectroscopy

Powder Technology ◽

10.1016/j.powtec.2004.01.025 ◽

2004 ◽

Vol 141 (3) ◽

pp. 244-250 ◽

Cited By ~ 84

Author(s):

Makoto Otsuka

Keyword(s):

Particle Size ◽

Regression Analysis ◽

Near Infrared ◽

Principal Component Regression ◽

Principal Component ◽

Size Determination ◽

Bulk Powder ◽

Principal Component Regression Analysis ◽

Particle Size Determination

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