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Authors = Radu E. Sestraş

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23 pages, 519 KiB  
Article
Poisson Parameters of Antimicrobial Activity: A Quantitative Structure-Activity Approach
by Radu E. Sestraş, Lorentz Jäntschi and Sorana D. Bolboacă
Int. J. Mol. Sci. 2012, 13(4), 5207-5229; https://doi.org/10.3390/ijms13045207 - 24 Apr 2012
Cited by 8 | Viewed by 8089
Abstract
A contingency of observed antimicrobial activities measured for several compounds vs. a series of bacteria was analyzed. A factor analysis revealed the existence of a certain probability distribution function of the antimicrobial activity. A quantitative structure-activity relationship analysis for the overall antimicrobial [...] Read more.
A contingency of observed antimicrobial activities measured for several compounds vs. a series of bacteria was analyzed. A factor analysis revealed the existence of a certain probability distribution function of the antimicrobial activity. A quantitative structure-activity relationship analysis for the overall antimicrobial ability was conducted using the population statistics associated with identified probability distribution function. The antimicrobial activity proved to follow the Poisson distribution if just one factor varies (such as chemical compound or bacteria). The Poisson parameter estimating antimicrobial effect, giving both mean and variance of the antimicrobial activity, was used to develop structure-activity models describing the effect of compounds on bacteria and fungi species. Two approaches were employed to obtain the models, and for every approach, a model was selected, further investigated and found to be statistically significant. The best predictive model for antimicrobial effect on bacteria and fungi species was identified using graphical representation of observed vs. calculated values as well as several predictive power parameters. Full article
(This article belongs to the Special Issue Advances in Biomolecular Simulation)
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18 pages, 247 KiB  
Communication
Pearson-Fisher Chi-Square Statistic Revisited
by Sorana D. Bolboacă, Lorentz Jäntschi, Adriana F. Sestraş, Radu E. Sestraş and Doru C. Pamfil
Information 2011, 2(3), 528-545; https://doi.org/10.3390/info2030528 - 15 Sep 2011
Cited by 102 | Viewed by 26136
Abstract
The Chi-Square test (χ2 test) is a family of tests based on a series of assumptions and is frequently used in the statistical analysis of experimental data. The aim of our paper was to present solutions to common problems when applying the [...] Read more.
The Chi-Square test (χ2 test) is a family of tests based on a series of assumptions and is frequently used in the statistical analysis of experimental data. The aim of our paper was to present solutions to common problems when applying the Chi-square tests for testing goodness-of-fit, homogeneity and independence. The main characteristics of these three tests are presented along with various problems related to their application. The main problems identified in the application of the goodness-of-fit test were as follows: defining the frequency classes, calculating the X2 statistic, and applying the χ2 test. Several solutions were identified, presented and analyzed. Three different equations were identified as being able to determine the contribution of each factor on three hypothesizes (minimization of variance, minimization of square coefficient of variation and minimization of X2 statistic) in the application of the Chi-square test of homogeneity. The best solution was directly related to the distribution of the experimental error. The Fisher exact test proved to be the “golden test” in analyzing the independence while the Yates and Mantel-Haenszel corrections could be applied as alternative tests. Full article
(This article belongs to the Section Information Theory and Methodology)
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