Index of papers in PLOS Comp. Biol. that mention

**predictive power**

Introduction | Even though we assumed specific molecular mechanisms underlying this collective antibiotic response, our model illustrates that the predictive power of the recovery time is maintained for different specific molecular mechanisms and for different initial conditions. |

Predictive power of recovery time for intravenous-drip protocols | Predictive power of recovery time for intravenous-drip protocols |

Predictive power of recovery time for intravenous-drip protocols | Thus, we also modeled the predictive power of the recovery time in intravenous (IV)drip based protocols, where a set concentration of antibiotic is delivered over a set duration during each dosing period. |

Predictive power of the recovery time for injection-based protocols | Predictive power of the recovery time for injection-based protocols |

Predictive power of the recovery time for injection-based protocols | We first tested the predictive power of the recovery time in injection-based dosing protocols. |

Predictive power of the recovery time for injection-based protocols | Our modeling results confirmed the predictive power of the recovery time: as long as the initial antibiotic concentration is sufficiently high to cause significant initial lysis, the population will reach a high final density if the period is greater than the recovery time; the population goes extinct otherwise (Fig. |

predictive power is mentioned in 24 sentences in this paper.

Topics mentioned in this paper:

- lysis (37)
- predictive power (24)
- subpopulations (14)

Conclusions | The accuracy of the proposed risk assessment analysis is stable across variations of the temporal correlations of the system, whereas its predictive power depends on the degree of memory kept in the time evolution. |

Loyalty | Ph, P, probability of a high(low) risk node to be infected a)?“ predictive power (fraction of infected nodes for which it is possible to compute the epidemic risk) |

Memory driven dynamical model | The observed differences in the predictive power of the approach are expected to be induced by the different temporal behavior of the two systems, resulting in a different amount of memory in preserving links (Fig. |

Memory driven dynamical model | In order to systematically explore the role of these temporal features on the accuracy and predictive power of our approach, we introduce a generic model for the generation of synthetic temporal networks. |

Memory driven dynamical model | In networks characterized by higher memory, the distribution of the predictive power (0 has a well defined peak, whereas for lower memory it is roughly uniform in the range (0 E [0, 0.4] (Fig. |

Validation | One other important aspect to characterize is the predictive power of our risk assessment analysis. |

Validation | We can then quantify the predictive power (0 as the fraction of infected nodes for which we could provide the epidemic risk, i.e. |

Validation | 4C-D display the distributions P(w) obtained for the two case studies, showing that a higher predictive power is obtained in the cattle trade network (peak at w 2 60%) with respect to the sexual contact network (peak at w 2 40%). |

predictive power is mentioned in 8 sentences in this paper.

Topics mentioned in this paper:

Acknowledgments | We thank Dr. Stephan Feller for providing the Raf-RBD construct, Dr. Bettina Hahn for the mass spectrometry measurements and Dr. Clemens Kreutz for the support for the analysis of the predictive power of the models. |

Ordinary differential equation model selection | We hypothesized that the advantage of a reduced model resides in an improved predictive power . |

Ordinary differential equation model selection | To compare the predictive power of the complete model and the model 4_8_12, we analyzed with the model the dynamic behaviour of a protein that has not been measured experimentally. |

Ordinary differential equation model selection | These results show that the selected reduced model 4_8_12 has a better predictive power than the complete model. |

Ordinary differential equation modeling | Predictive power . |

Ordinary differential equation modeling | To compare the predictive power of the selected candidate model 4_8_12 and the complete model, we utilize prediction profiles as described [39]. |

predictive power is mentioned in 6 sentences in this paper.

Topics mentioned in this paper:

- phosphorylation (52)
- signaling pathways (32)
- MAPK (29)

Introduction | The coordination of the moth’s downstroke muscles is a very simple synergy hypothesis: that one variable describing the combination of these two muscles’ activities has as much predictive power as considering the two muscles independently. |

Synergy model testing | If the two-variable independence model has greater predictive power than the one-variable synergy or redundancy models, then each muscle contributes significantly to the decoding of torque. |

Torque waveform reconstruction | To test the predictive power of the reconstructions, we cross-validated the feature analysis using 70% of each decile of the data as a training set to predict the remaining 30%. |

predictive power is mentioned in 3 sentences in this paper.

Topics mentioned in this paper: