Uncertain Stochastic Modeling

Cross-source consensus on Uncertain Stochastic Modeling from 1 sources and 5 claims.

1 sources · 5 claims

How it works

The proposed model combines probabilistic noise with possibilistic parameter perturbation. — Identifying the Attractors of Gene Regulatory Networks from Expression Data under Uncertainty: An Interpretable Approach
Probabilistic uncertainty is represented by adding noise terms to the differential equations. — Identifying the Attractors of Gene Regulatory Networks from Expression Data under Uncertainty: An Interpretable Approach
Possibilistic uncertainty is represented as uncertainty in synthesis, cooperativity, and degradation parameters. — Identifying the Attractors of Gene Regulatory Networks from Expression Data under Uncertainty: An Interpretable Approach
The four in-silico tests use 20 independent uncertain stochastic runs to generate temporal expression profiles. — Identifying the Attractors of Gene Regulatory Networks from Expression Data under Uncertainty: An Interpretable Approach
The combined uncertain stochastic model produces more complex temporal profiles and phase trajectories than the nominal deterministic model. — Identifying the Attractors of Gene Regulatory Networks from Expression Data under Uncertainty: An Interpretable Approach