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Table 2 Scores and features of parameter inference challenge

From: Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach

Model 1 Parameter distance Dparam P-value for parameter predictions Protein distance Dprot P-value for protein time course predictions Score Bayesian Decompose network" Selection of data Sampling
Orangeballs 0.0229 3.25E-03 0.002438361 1.21E - 25 27.4 no yes Game Tree Sequential local search
2 0.8404 1.00E + 00 0.016023721 3.39E-18 17.5 no no Manual based on parameter uncertainty Global method
3 0.1592 6.00E-01 0.035404398 4.45E-15 14.6 yes no Manual LH
4 0.0899 1.88E-01 0.047495432 6.28E-14 13.9 no yes Manual LM + Particle Swarm
5 0.1683 6.45E-01 0.09791128 4.01E-11 10.6 yes no Train + Sim UKF
6 0.0453 1.37E-02 0.198785197 1.93E-08 9.6 no no A=Criterion Local (LM)
7 0.1702 6.45E-01 0.362463945 2.90E-06 5.7 no yes Sensitivity analysis Hybrid (Local + Global)
8 0.8128 1.00E + 00 0.356429217 2.53E-06 5.6 yes no Estimation of improved uncertainty Global (MH)
9 0.3766 9.99E-01 0.817972877 1.34E-03 2.9 yes yes MI ABC-SMC
10 0.0699 9.83E-02 19.32326868 1.00E + 00 1.0 no yes Minimize variance based on FI Multistart local search
11 0.1883 7.29E-01 3.222767988 6.90E-01 0.3 no no Train + Sim LH + DE
12 5.0278 1.00E + 00 14.77443631 1.00E + 00 0.0 no no Manual Local method
  1. Table for Model 1 of the parameter inference challenge contains anonymized teams (except for best performer) ordered by Score rank. Next to each team is listed its parameter distance and associated p-value, protein distance and associated p-value and the score. The last four columns indicate the features of the fitting strategies used by the participants. Abbreviations used for the features: ABC-SMC, Approximate Bayesian Computation with Sequential Monte Carlo; DE, Differential Evolution; FI, Fisher Information; LH, Latin Hypercube; LM, Levenberg-Marquardt; MH, Metropolis Hastings; MI, Maximize Mutual Information between parameters and output of experiments; Train + Sim, iterative steps of training on data and simulation to find most informative experiments; Rank rank experiments in top 10% of the A-Criterion (trace of the covariance matrix) according to price; UKF, Unscented Kalman Filtering.