Topological models for prediction of physico-chemical, pharmacokinetic and toxicological properties of antihistaminic drugs using decision tree and moving average analysis

2009 ◽  
Vol 2 (4) ◽  
pp. 353 ◽  
Author(s):  
Harish Dureja ◽  
Sunil Gupta ◽  
A.K. Madan
Keyword(s):  
Decision Tree ◽  
Moving Average ◽  
Physico Chemical ◽  
Average Analysis ◽  
Antihistaminic Drugs ◽  
Topological Models

(Q)SPR Models for Prediction of Hydrophobicity of Isatins

2018 ◽  
Vol 3 (1) ◽  
pp. 88-99
Author(s):  
A.K. Madan ◽  
Rohit Dutt
Keyword(s):  
Decision Tree ◽  
Correlation Coefficient ◽  
Moving Average ◽  
Topological Descriptors ◽  
Design And Development ◽  
Single Index ◽  
Average Analysis ◽  
Highly Hydrophobic ◽  
Sensitivity Specificity ◽  
Accuracy Of Prediction

In the present study, the application of a wide variety of topological descriptors was investigated for predicting hydrophobicity (clogP) of isatin analogues. A total of four topochemical indices selected through decision tree (DT) were used for the development of single index based models using moving average analysis (MAA). The overall accuracy of prediction varied from a minimum of 95% to a maximum of 98% with regard to hydrophobicity.The values of sensitivity, specificity and Mathew's correlation coefficient for all MAA based models with regard to hydrophobicity (clogP) was found to be =78%, =94% and =0.85 respectively, suggesting robustness of proposed models. Since the compounds with high clogP values were found effective in carboxylesterases (CEs) inhibition, therefore, highly hydrophobic ranges of proposed MAA models can easily be exploited for the design and development of potent CEs inhibitors.

scholarly journals Auto-regressive moving average analysis of linear and discontinuous models of human balance during quiet standing

2014 ◽  
Vol 24 (2) ◽  
pp. 022101 ◽  
Author(s):  
Piotr Kowalczyk ◽  
Salam Nema ◽  
Paul Glendinning ◽  
Ian Loram ◽  
Martin Brown
Keyword(s):  
Moving Average ◽  
Quiet Standing ◽  
Average Analysis ◽  
Human Balance ◽  
Auto Regressive

scholarly journals Modified moving average analysis of T-wave alternans to predict ventricular fibrillation with high accuracy

2002 ◽  
Vol 92 (2) ◽  
pp. 541-549 ◽  
Author(s):  
Bruce D. Nearing ◽  
Richard L. Verrier
Keyword(s):  
Ventricular Fibrillation ◽  
Moving Average ◽  
Motion Artifact ◽  
Accurate Estimate ◽  
T Wave ◽  
Average Analysis ◽  
Experimental Laboratory ◽  
Nonstationary Data ◽  
T Wave Alternans ◽  
And Performance

T-wave alternans is a marker of cardiac electrical instability with the potential for arrhythmia risk stratification. The modified moving average method was developed to measure alternans in settings with artifacts, noise, and nonstationary data. Algorithms were developed and performance characteristics were validated with simulated electrocardiograms (ECGs). Experimental laboratory ECGs with dynamically changing alternans values were analyzed. Alternans values estimated by modified moving average analysis correlated strongly with input alternans values ( r 2 = 0.9999). Rapidly changing alternans levels and phase reversals did not perturb the measurement. When heart rate was increased from 60 to 180 beats/min, with T-wave alternans apex moving from 237 to 103 ms after the R wave, the measured alternans peak varied <5% from input value. Simulated 50- to 1,000-μV motion artifact spikes typical of treadmill ECGs produced inaccuracies <2%. Alternans values in experimental laboratory study using standard electrodes tracked vulnerability to myocardial ischemia-induced ventricular fibrillation with 100% sensitivity and specificity at a cut point of 0.75 mV. Modified moving average analysis is a robust method that precisely measures T-wave alternans in settings with artifacts, noise, and nonstationary data typical of clinical ECGs and yields an accurate estimate of risk for ventricular fibrillation.

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