Bayesian ranking of biochemical system models

Bayesian ranking of biochemical system models

MOTIVATION There often are many alternative models of a biochemical system. Distinguishing models and finding the most suitable ones is an important challenge in Systems Biology, as such model ranking, by experimental evidence, will help to judge the support of the working hypotheses forming each model. Bayes factors are employed as a measure of evidential preference for one model over another....

Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models

As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we explore the Bayesian estimation of stochastic kinetic rate constants governing dynamic models of intracellular processes. The underlying model is replaced by a diffusion approximation where a noise term represents intrinsi...

Stochastic models for an open biochemical system.

This paper uses the theory of Markov processes to derive stochastic models for a single open biochemical system at steady state under 3 sets of assumptions. The system is a one substrate, one product reaction. Each set of assumptions re:mlts in a separate solution for the probability functions. A system of linear equations in the probability function as well as an equivalent differential equati...

Naive Bayesian Classifiers for Ranking

It is well-known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. In many applications, however, a ranking based on class probabilities is desired. For example, a ranking of customers in terms of the likelihood that they buy one’s products is useful in direct marketing. What is the general performance of naive Bayes in ranking? In this paper...

Ranking of DMUs by using TOPSIS and dierent ranking models in DEA