Index of papers in PLOS Comp. Biol. that mention
• normal distribution
Dimitri Yatsenko, Krešimir Josić, Alexander S. Ecker, Emmanouil Froudarakis, R. James Cotton, Andreas S. Tolias
 Functional connectivity as a network of pairwise interactions Assuming that the population response follows a multivariate normal distribution , the conditional dependencies between pairs of neurons are expressed by the partial correlations between them. Model selection We evaluated the covariance matriX estimators using a loss function derived from the normal distribution . Model selection However, this does not limit the applicability of its conclusions to normal distributions . Simulation Here, in the role of the loss function we adopted the Kullback-Leibler divergence between multivariate normal distributions with equal means, scaled by; to make its values comparable across different population sizes: Simulation For simulation, ground truth covariance matrices were produced by taking 150 independent samples from an artificial population of 50 independent, identically normally distributed units. Simulation Samples were then drawn from multivariate normal distributions models with the respective true covariance matrices to be estimated by each of the estimators.
normal distribution is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Xiliang Zheng, Jin Wang
 Analytical Models of Distribution of Affinity, Equilibrium Constants, Specificity and Kinetics Based on the probability theory and statistics, if the random variable X is log-normally distributed, then Y = log(X) has a normal distribution . Analytical Models of Distribution of Affinity, Equilibrium Constants, Specificity and Kinetics Likewise, if Y has a normal distribution , then X = exp(Y) has a log-normal distribution. Analytical Models of Distribution of Affinity, Equilibrium Constants, Specificity and Kinetics Therefore, there is a physical explanation of the difference in distributions of binding kinetics: Above the characteristic transition temperature Tc, there are multiple parallel kinetic paths, each experiencing certain barriers (barrier has a normal distribution ), resulting in log-normal kinetics (seen also in protein conformational dynamics simulations ). Microscopic Atomic Binding Model and Simulation Results We see that the logarithm of equilibrium constant can be fitted well with a normal distribution near the mean while near the tail can be fitted well with a exponential distribution. Microscopic Atomic Binding Model and Simulation Results We also see that the distribution of the intrinsic specificity can be fitted well with the normal distribution near the mean and also well fitted with the exponential distribution at the tail.
normal distribution is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Christiaan A. de Leeuw, Joris M. Mooij, Tom Heskes, Danielle Posthuma
 Analysis of summary SNP statistics A random phenotype is first generated for the reference data, drawing from the standard normal distribution . Gene-set analysis This yields a roughly normally distributed variable Z with elements zg that reflects the strength of the association each gene has with the phenotype, with higher values corresponding to stronger associations. Gene-set analysis Evaluating fig 2 0 against the alternative fig > 0 yields a self-contained test, since under the self-contained null hypothesis that none of the genes is associated with the phenotype zg has a standard normal distribution for every gene g. Competitive gene-set analysis tests whether the genes in a gene-set are more strongly associated with the phenotype of interest than other genes. Gene-set analysis One complication that arises in this gene-level regression framework is that the standard linear regression model assumes that the error terms have independent normal distributions , i.e.
normal distribution is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
James A. Geraets, Eric C. Dykeman, Peter G. Stockley, Neil A. Ranson, Reidun Twarock
 Analysis of the tomogram via graph theory As discussed in Methods, we attributed tomographic density to each of the 25 long edges of the polyhedral cage representing the icosahedrally-averaged density considered in this analysis and fitted it to a normal distribution . Analysis of the tomogram via graph theory A ranking of the level of density associated with these edges was achieved using the mean of the fitted normal distribution . The density profiles of the long edges We computed fitted normal distributions using the normfit function from the scipy.stats python library, since for a sparse dataset the mean of a fitted normal distribution is less affected by outliers than the raw data. The density profiles of the long edges The normal fitting function automatically calculated the best positioning of a unimodal normal distribution for the dataset.
normal distribution is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Giulia Menconi, Andrea Bedini, Roberto Barale, Isabella Sbrana
 Flexibility values and peaks 7 (top picture) shows the normalized distribution of windows flexibility values for all 16 chromosomes of yeast genome. Statistical analysis A: Flexibility values normalized distribution for all the yeast chromosomes. Statistical analysis It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known.
normal distribution is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Tom Lindström, Michael Tildesley, Colleen Webb
 Adapting the Tebaldi et al. method for emerging diseases The relationship between projections and ensemble parameters is eXpressed as with Normal([,t,7t,-'1) denoting the normal distribution with mean [,4 and variance M1. Computation Here MVN indicates the multivariate normal distribution and ET the covariance matriX. Informative Hierarchical Weighting model To demonstrate the effect that a priori trust in different modeling assumptions can have on the posterior estimates, we consider the case where the best, most likely and worst case scenarios for each of the two varied parameters corresponds to percentile 2.5, 50 and 97.5, respectively, of a normal distribution .
normal distribution is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Nancy K. Drew, Mackenzie A. Eagleson, Danny B. Baldo Jr., Kevin Kit Parker, Anna Grosberg
 Introduction Others use the von Mises circular distribution [9, 10], which is a wrapped normal distribution . OOPPCOOPC. Assuming OOPp and OOPQ are normally distributed with the standard deviation UOOPP and O'OOPQ, respectively, and OOPP and OOPQ are independent, then the variance: and the standard deviation: Synthetic Data 3) contained 108 random numbers (MATLAB function normrnd) that were normally distributed With the specified mean and standard deviation.
normal distribution is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
William F. Flynn, Max W. Chang, Zhiqiang Tan, Glenn Oliveira, Jinyun Yuan, Jason F. Okulicz, Bruce E. Torbett, Ronald M. Levy
 Pairwise covariation However, Kendall's tau is not appropriate for this type of data because Kendall's tau typically requires the underlying population to be bivariate normal; the frequencies we observe are not normally distributed . The pooled proportion PP _PS >
normal distribution is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: