Comparison of Linear and Nonlinear Classification Algorithms for the Prediction of Drug and Chemical Metabolism by Human UDP-Glucuronosyltransferase Isoforms

2003 ◽  
Vol 43 (6) ◽  
pp. 2019-2024 ◽  
Author(s):  
Michael J. Sorich ◽  
John O. Miners ◽  
Ross A. McKinnon ◽  
David A. Winkler ◽  
Frank R. Burden ◽  
...  
Keyword(s):  
Classification Algorithms ◽  
Nonlinear Classification ◽  
Linear And Nonlinear ◽  
Udp Glucuronosyltransferase

Comparison of Linear and Nonlinear Classification Algorithms for the Prediction of Drug and Chemical Metabolism by Human UDP-Glucuronosyltransferase Isoforms.

ChemInform ◽  
2004 ◽  
Vol 35 (5) ◽  
Author(s):  
Michael J. Sorich ◽  
John O. Miners ◽  
Ross A. McKinnon ◽  
David A. Winkler ◽  
Frank R. Burden ◽  
...  
Keyword(s):  
Classification Algorithms ◽  
Nonlinear Classification ◽  
Linear And Nonlinear ◽  
Udp Glucuronosyltransferase

Classification of Activated Sludge Settleability Using Linear and Nonlinear Classification Functions

2008 ◽  
Vol 41 (2) ◽  
pp. 14600-14605
Author(s):  
Geert Gins ◽  
Jef Vanlaer ◽  
Ilse Y. Smets ◽  
Jan F. Van Impe
Keyword(s):  
Activated Sludge ◽  
Nonlinear Classification ◽  
Linear And Nonlinear ◽  
Sludge Settleability

Linear and nonlinear classification models for tea grade identification based on the elemental profile

2020 ◽  
Vol 153 ◽  
pp. 104512 ◽  
Author(s):  
Liuwei Meng ◽  
Xiaojing Chen ◽  
Xi Chen ◽  
Leiming Yuan ◽  
Wen Shi ◽  
...  
Keyword(s):  
Classification Models ◽  
Nonlinear Classification ◽  
Elemental Profile ◽  
Linear And Nonlinear

Spectral and Topographic Microstructure of Brain Alpha Activity During Drowsiness at Sleep Onset and REM Sleep

2000 ◽  
Vol 14 (3) ◽  
pp. 151-158 ◽  
Author(s):  
José Luis Cantero ◽  
Mercedes Atienza
Keyword(s):  
Rem Sleep ◽  
Spectral Component ◽  
Sleep Onset ◽  
Quantitative Information ◽  
Maximum Energy ◽  
Alpha Activity ◽  
The Other ◽  
Classification Algorithms ◽  
Microstructural Properties ◽  
Brain States

Abstract High-resolution frequency methods were used to describe the spectral and topographic microstructure of human spontaneous alpha activity in the drowsiness (DR) period at sleep onset and during REM sleep. Electroencephalographic (EEG), electrooculographic (EOG), and electromyographic (EMG) measurements were obtained during sleep in 10 healthy volunteer subjects. Spectral microstructure of alpha activity during DR showed a significant maximum power with respect to REM-alpha bursts for the components in the 9.7-10.9 Hz range, whereas REM-alpha bursts reached their maximum statistical differentiation from the sleep onset alpha activity at the components between 7.8 and 8.6 Hz. Furthermore, the maximum energy over occipital regions appeared in a different spectral component in each brain activation state, namely, 10.1 Hz in drowsiness and 8.6 Hz in REM sleep. These results provide quantitative information for differentiating the drowsiness alpha activity and REM-alpha by studying their microstructural properties. On the other hand, these data suggest that the spectral microstructure of alpha activity during sleep onset and REM sleep could be a useful index to implement in automatic classification algorithms in order to improve the differentiation between the two brain states.

M. Gilbert und Atazanavir: Vom Phänotyp zum UDP-glucuronosyltransferase Haplotyp

2006 ◽  
Vol 44 (08) ◽  
Author(s):  
TO Lankisch ◽  
U Moebius ◽  
M Wehmeier ◽  
G Behrens ◽  
MP Manns ◽  
...  
Keyword(s):  
Udp Glucuronosyltransferase
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