Conclusions | For both simulated data and a circadian metadataset [27] the resulting empirical ITK_CYCLE with asymmetry search eXhibits the greatest sensitivity among the methods that we evaluated. |
Discussion | They test the methods with simulated data and experimental data for the metabolic cycle in yeast, circadian rhythms in the mouse, and the root clock in the flowering plant Arabidopsis thaliana (see [28] for references). |
Discussion | They show that RAIN outperforms the original ITK_CYCLE method as well as a cosine-fitting method [62] for simulated data consisting of sinusoidal and ramp waveforms. |
Introduction | The simulated data allow us to examine how performance varies with sampling density, number of replicates and/ or periods, noise level, and waveform. |
Simulated data benchmarks | Simulated data benchmarks |
Simulated data benchmarks | Examples of simulated data . |
Simulated data benchmarks | shows that the ITK_CYCLE methods have higher-quality classification ability than F24 and ANOVA for these simulated data . |
Supporting Information | AUROCs for simulated data with 25% noise (standard deviation of Gaussian noise as a percent of amplitude). |
Supporting Information | Simulated data With rhythmic time series Without asymmetry (A) or With evenly distributed asymmetry (B) was tested With different methods. |
Supporting Information | Simulated data With rhythmic time series Without asymmetry (left, A and C) or With evenly distributed asymmetry (right, B and D) was tested With different asymmetries. |
Discussion | Because we used simulated data to infer parameters, and hence had knowledge of the true model, we were also assuming that these contacts were reported accurately. |
Estimating transmissibility and pre-existing immunity | We simulated data using different assumptions about age-specific infection rates but left the inference model unchanged. |
Estimating transmissibility and pre-existing immunity | Next, we simulated data using two ages groups, but with transmission based on the average number of reported physical contacts across 8 European countries in the POLYMOD study (S1B Fig. |
Characteristic power-law parameters describe uptake in different cell lines | Changes in many kinetic parameters associated with the Zipper mechanism can be mapped to changes in the power-law parameters (Fig 4A; simulated data in 84C Fig). |
Log 1IKM | Simulated data presented in S4C Fig (B) Power-law parameters for different hosts and bacterial strains. |
Supporting Information | (Left) Relationship between bacterial uptake and A-R. (Right) Relationship between probability (uptake/MOI) and AR calculated from simulated data . |
A phenomenological model | By directly fitting tuft-constant planes of the simulation data (horizontal planes of Fig. |
A phenomenological model | The sigmoidal functions found by fitting the form of the entire composite equation to the simulation data fit the extracted M and T values well (Fig. |
A phenomenological model | This points to the strength of the composite model, as the same parameters can be found from the simulation data in two different ways. |
Comparison of analytical results with simulations data | Comparison of analytical results with simulations data |
Comparison of analytical results with simulations data | 3 compares the full analytical solution (Equation 7, with H given by Equation 10) against simulation data . |
Simulation methods | We verified our analytical solution by comparing it to simulation data . |