(A) Hypothesis assessment of model topologies, per dataset. Probability indicates the result of Bayes theorem using equivalent prior probabilities per topology (e.g., 9% probability that one of the topologies in the x-axis best represents a dataset) and Bayesian evidence values (marginal likelihoods) summed per topology. Model topologies represented by images and corresponding numbers along the x-axis. Posterior probability based on marginal likelihoods of all candidate models that include A as an initiating subtype. (B) Division and phenotypic transition parameters for TKO, RPM, and SCLC-A cell line datasets, comparing between higher-probability topologies (A) and four-subtype topology per dataset. Red arrowheads indicate higher A-to-A2 tr...
<p>Each row represents a phenotype and consists of three cells, representing (a) model predictions b...
In the analysis of any data using statistical modelling, it is imperative that the choice of model i...
Columns show different versions of the task. Rows show model fits for (A) the distance model (unscal...
(A) Hypothesis assessment of model topologies per dataset, posterior probabilities based on all cand...
(A) Heatmap for high probability three-subtype topologies for each dataset (rows), all models initia...
Hypothesis assessment of tumor-iniating subtypes, per dataset. Probability indicates the result of B...
(A) Aspects of linear regression model assessed by model selection and model averaging (see Fig 1A)....
(A) Rate of a cell fate (division, death, or phenotypic transition) for x (vfate) can be calculated ...
Model term posterior probabilities after hypothesis exploration, SCLC-A cell line data high-prob. 3-...
Model term posterior probabilities after hypothesis exploration, RPM high-probability 3-subtype topo...
Model term posterior probabilities after hypothesis exploration, TKO high-probability 3-subtype topo...
(A and B) Disease progression phenotypes of (A) patients with local progression without demonstratin...
Abstract.—Probabilistic tests of topology offer apowerfulmeansof evaluating competing phylogenetic h...
<p>Tumor size distribution in predictive models, (a) Stage N0,M0 in SEER (2004–2008) and model (1988...
<p>A) Distribution of pattern types exhibited by units in the 2- and 4-population spiking models (le...
<p>Each row represents a phenotype and consists of three cells, representing (a) model predictions b...
In the analysis of any data using statistical modelling, it is imperative that the choice of model i...
Columns show different versions of the task. Rows show model fits for (A) the distance model (unscal...
(A) Hypothesis assessment of model topologies per dataset, posterior probabilities based on all cand...
(A) Heatmap for high probability three-subtype topologies for each dataset (rows), all models initia...
Hypothesis assessment of tumor-iniating subtypes, per dataset. Probability indicates the result of B...
(A) Aspects of linear regression model assessed by model selection and model averaging (see Fig 1A)....
(A) Rate of a cell fate (division, death, or phenotypic transition) for x (vfate) can be calculated ...
Model term posterior probabilities after hypothesis exploration, SCLC-A cell line data high-prob. 3-...
Model term posterior probabilities after hypothesis exploration, RPM high-probability 3-subtype topo...
Model term posterior probabilities after hypothesis exploration, TKO high-probability 3-subtype topo...
(A and B) Disease progression phenotypes of (A) patients with local progression without demonstratin...
Abstract.—Probabilistic tests of topology offer apowerfulmeansof evaluating competing phylogenetic h...
<p>Tumor size distribution in predictive models, (a) Stage N0,M0 in SEER (2004–2008) and model (1988...
<p>A) Distribution of pattern types exhibited by units in the 2- and 4-population spiking models (le...
<p>Each row represents a phenotype and consists of three cells, representing (a) model predictions b...
In the analysis of any data using statistical modelling, it is imperative that the choice of model i...
Columns show different versions of the task. Rows show model fits for (A) the distance model (unscal...