Index of papers in PLOS Comp. Biol. that mention
  • predictive power
Hannah R. Meredith, Allison J. Lopatkin, Deverick J. Anderson, Lingchong You
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:
Eugenio Valdano, Chiara Poletto, Armando Giovannini, Diana Palma, Lara Savini, Vittoria Colizza
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:
Lorenza A. D’Alessandro, Regina Samaga, Tim Maiwald, Seong-Hwan Rho, Sandra Bonefas, Andreas Raue, Nao Iwamoto, Alexandra Kienast, Katharina Waldow, Rene Meyer, Marcel Schilling, Jens Timmer, Steffen Klamt, Ursula Klingmüller
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:
Simon Sponberg, Thomas L. Daniel, Adrienne L. Fairhall
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: