Index of papers in PLOS Comp. Biol. that mention
  • correlation coefficient
Dimitri Yatsenko, Krešimir Josić, Alexander S. Ecker, Emmanouil Froudarakis, R. James Cotton, Andreas S. Tolias
Covariance estimation
Where diag(C) denotes the diagonal matrix With the diagonal elements from C. The partial correlation between a pair of variables is the Pearson correlation coefficient of the residuals of the linear least-squares predictor of their activity based on all the other variables, excluding the pair [40, 51].
Introduction
For example, the eigenvalue decomposition of the covariance matrix expresses shared correlated activity components across the population; common fluctuations of population activity may be accurately represented by only a few eigenvec-tors that affect all correlation coefficients .
Introduction
The partial correlation coefficient between two neurons reflects their linear association conditioned on the activity of all the other recorded cells [40].
Introduction
In our data, the sample correlation coefficients were largely positive and low.
The Csparse+latent estimator is most efficient in neural data
The sample correlation coefficients were largely positive and low (Fig.
The Csparse+latent estimator is most efficient in neural data
The average value of the correlation coefficient across sites ranged from 0.0065 to 0.051 with the mean across sites of 0.018.
The Csparse+latent estimator is most efficient in neural data
F. Histogram of noise correlation coefficients in one site.
correlation coefficient is mentioned in 8 sentences in this paper.
Sébastien Giguère, François Laviolette, Mario Marchand, Denise Tremblay, Sylvain Moineau, Xinxia Liang, Éric Biron, Jacques Corbeil
Improving the bioactivity of peptides
Despite this small discrepancy, the model is very accurate on the training data ( correlation coefficient of 0.97).
Simulation of a drug discovery
The Pearson correlation coefficient (PCC, also known as the Pearson’s r) was computed between hmndam predictions and the values in both databases.
Simulation of a drug discovery
Correlation coefficients are shown in the last column of Table 1.
Simulation of a drug discovery
When initiated with R = 1,000 random peptides, it achieves a correlation coefficient of 0.90 (CAMPs) and 0.93 (BPP).
correlation coefficient is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Ickwon Choi, Amy W. Chung, Todd J. Suscovich, Supachai Rerks-Ngarm, Punnee Pitisuttithum, Sorachai Nitayaphan, Jaranit Kaewkungwal, Robert J. O'Connell, Donald Francis, Merlin L. Robb, Nelson L. Michael, Jerome H. Kim, Galit Alter, Margaret E. Ackerman, Chris Bailey-Kellogg
Supervised learning: Regression
The models are clearly predictive of ADCP, obtaining a mean Pearson correlation coefficient PCC = 0.64 (standard deviation 0.15) over the 200-replicate fivefold.
Supervised learning: Regression
Performance was assessed by Pearson correlation coefficient (PCC), r, between observed and predicted function value; r assesses the linear correlation (between -1 for perfectly anticor-related and +1 for perfectly correlated), while 1'2 represents the fraction of the variation eXplained.
Unsupervised learning
Filtered features were selected by choosing the feature most strongly correlated with the function within each cluster, in terms of the magnitude of the Pearson correlation coefficient (Fig 2A).
Unsupervised learning
Antibody feature:function and feature:feature correlations were computed over the set of 80 vaccinated subjects and assessed using Pearson correlation coefficient and p-value.
Unsupervised learning
Features were clustered based on the profile of their correlation coefficients over the set of all features.
correlation coefficient is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Nancy K. Drew, Mackenzie A. Eagleson, Danny B. Baldo Jr., Kevin Kit Parker, Anna Grosberg
Discussion
Circular statistics tool-sets include some correlation metrics [24], such as the circular correlation coefficient [21] which corresponds to the COOP in the same case.
Discussion
Specificically the circular correlation coefficient can only be used for uniform distributions (i.e.
Discussion
In that special case, the circular correlation coefficient and the COOP converge to the same equation (SI Supplemental Text).
correlation coefficient is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
David Lovell, Vera Pawlowsky-Glahn, Juan José Egozcue, Samuel Marguerat, Jürg Bähler
Alternative measures of proportionality
While goodness-of—fit measures for regression may not generally be appropriate for assessing proportionality, Zheng [28] eXplores the concordance correlation coefficient pC [29] which could be modified to provide an alternative measure of proportionality defined as and related to var(log(x/y)) by the terms in Equation 1.
Caution about correlation
Currently, there are many gene co-expression databases available that provide correlation coefficients for the relative expression levels of different genes, generally from multiple experiments with different experimental conditions (see e.g., [18]).
Supporting Information
A 2D histogram of the correlation coefficient observed for the relative abundances of a given pair of mRNAs in a sample where the ten most abundant mRNAs have been removed, against the correlation coefficient observed for the relative abundances of that same pair, over all pairs.
Supporting Information
While the distribution of the correlation coefficient pairs lies more on the diagonal than in the preceding figure, it is clear that correlation of relative abundances is sensitive to What is in (or out of) the
Supporting Information
A 2D histogram of ¢(clr(xi), clr(xj)) for the relative abundances of a given pair (i, j) of mRNAs, against the correlation coefficient observed for the absolute abundances of that same pair, over all pairs.
correlation coefficient is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Thomas W. Spiesser, Clemens Kühn, Marcus Krantz, Edda Klipp
Results
Black line indicates a least-squares regression with correlation coefficient (R) and coefficient of determination (R2).
Supporting Information
Black line indicates a least-squares regression With correlation coefficient (R) and coefficient of determination (R2).
Supporting Information
Lines (Model-1: red; Model-2: blue) indicate least-squares regressions With respective correlation coefficient (R) and coefficient of determination (R2).
Supporting Information
Lines (Model-1: red; Model-2: blue) indicate least-squares regressions with respective correlation coefficient (R) and 810 Fig.
correlation coefficient is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Joon-Young Moon, UnCheol Lee, Stefanie Blain-Moraes, George A. Mashour
Confirmation of node degree/directionality relationship in a computational model of human brain networks
Fig 4A and 4C clearly demonstrate a negative correlation between node degree and dPLI (Spearman correlation coefficient = - 0.61, p< 0.01) and positive correlation between node degree and amplitude of oscillators (Spearman correlation coefficient = 0.92, p<0.01) at coupling strength 8 = 3.
Confirmation of node degree/directionality relationship in human EEG networks during conscious and unconscious states
The strong negative correlation observed during the conscious state (Spearman correlation coefficient of -O.76 (p<0.01)) disappears during the unconscious state (Spearman correlation coefficient of -0.04 (p<0.01)).
Confirmation of node degree/directionality relationship in human EEG networks during conscious and unconscious states
However, the correlation between node degree and amplitude for the EEG network differs from the models (nonsignificant Spearman correlation coefficient of 0.266 (p = 0.1) for the conscious state).
Human EEG network analysis
The spearman correlation coefficient was used for evaluating the correlations among node degree, amplitude and dPLI of the 64 channels (“corr.m” in Matlab).
correlation coefficient is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Alan L. Hutchison, Mark Maienschein-Cline, Andrew H. Chiang, S. M. Ali Tabei, Herman Gudjonson, Neil Bahroos, Ravi Allada, Aaron R. Dinner
Simulated data benchmarks
Correlation Coefficient [39], which quantifies the quality of a binary classification.
Supporting Information
Matthews Correlation Coefficient shows that IT K_CYCLE methods outperform ANOVA and F24 in the presence and absence of asymmetric time series.
Supporting Information
The vertical axis shows the Matthews Correlation Coefficients (MCC) [39] for different Benjamini-Hochberg adjusted p-value cutoffs (FDR) along the X-aXis.
correlation coefficient is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: