Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Global Minimization of a Multivariate Polynomial using Matrix Methods
Journal of Global Optimization
Exact parameter determination for Parkinson's disease diagnosis with PET using an algebraic approach
AB'07 Proceedings of the 2nd international conference on Algebraic biology
| Hi-index | 0.00 |
We propose a novel algorithm to select a model that is consistent with the time series of observed data. In the first step, the kinetics for describing a biological phenomenon is expressed by a system of differential equations, assuming that the relationships between the variables are linear. Simultaneously, the time series of the data are numerically fitted as a series of exponentials. In the next step, both the system of differential equations with the kinetic parameters and the series of exponentials fitted to the observed data are transformed into the corresponding system of algebraic equations, by the Laplace transformation. Finally, the two systems of algebraic equations are compared by an algebraic approach. The present method estimates the model's consistency with the observed data and the determined kinetic parameters. One of the merits of the present method is that it allows a kinetic model with cyclic relationships between variables that cannot be handled by the usual approaches. The plausibility of the present method is illustrated by the actual relationships between specific leaf area, leaf nitrogen and leaf gas exchange with the corresponding simulated data.