Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting.ByHarvey Motulskyand, Arthur Christopoulos.Oxford and New York: Oxford University Press.…

By R. Maass-Moreno. 2005

258 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Health, Toxicology and Mutagenesis, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0e6378324aa755248d325246308a8d3463d55909

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Citations in the corpus

International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. XXXVIII. Update on Terms and Symbols in Quantitative Pharmacology (2003, background): “where pEC50 and pA2 are as defined previously in Tables 3 and 4, respectively, [B] denotes the antagonist concentration, S is a logistic slope factor analogous to the Schild slope and log c is a fitting constant (Motulsky and Christopoulos, 2003).”
Computation of measures of effect size for neuroscience data sets (2011, methodology): “A good approach would be to ﬁt a function to the data; for example, exponentials could be ﬁtted to the phases of decaying ﬁring rates and the ﬁtted parameter(s) used to judge on the difference between the conditions (Motulsky & Christopoulos, 2003).”
Methodical fitting for mathematical models of rubber-like materials (2016, background): “A possible consequence is that a set p = [ C 1 , C 2 , J m ] or [ C 1 , C 2 , β ] could be wrongly identiﬁed as the optimal one, while there exist other minima giving a better or similar error (Ogden et al. 2004, Motulsky and Christopoulos 2004).”
New Insights into Soybean Biological Nitrogen Fixation (2018, methodology): “1A, B, C), with Gaussian models fitted for each NDFA group (GraphPad Prism 6; Motulsky and Christopoulos, 2003).”
Soybean Sowing Date: The Vegetative, Reproductive, and Agronomic Impacts (2008, unknown): “Prism 4 software (Motulski and Christopoulos, 2003) using this (componentized) GraphPad equation: Y1 = I1 + B1 × X, Y at 3.”
Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth (2014, methodology): “J = 3 4 an im al s. d o i:1 0 .1 3 7 1 /j o u rn al .p cb i.1 0 0 3 8 0 0 .t 0 0 2 PLOS Computational Biology | www.ploscompbiol.org 8 August 2014 | Volume 10 | Issue 8 | e1003800 AICcpop gave a weaker importance to the number of parameters, due to the large number of data points in the setting of t”