We describe the techniques used to model genetic and biochemical networks, together with the computational tools needed for stochastic simulation and analysis. An overview is also given of the MCMC algorithms which can be used for carrying out Bayesian inference for the parameters underlying the network models, and the problems associated with applying such techniques i
The two main aims of this thesis is initially to develop a new frequentist gene expression index (FO...
<div><p>We compare three state-of-the-art Bayesian inference methods for the estimation of the unkno...
We compare three state-of-the-art Bayesian inference methods for the estimation of the unknown param...
Computational systems biology is concerned with the development of detailed mechanistic models of bi...
Mathematical modelling opens the door to a rich pathway to study the dynamic properties of biologica...
Gene regulatory networks are a visual representation of genes and their interactions. In this visual...
In this paper we investigate Monte Carlo methods for the approximation of the posterior probability ...
In this paper, we apply Bayesian networks (BN) to infer gene regulatory network (GRN) model from gen...
This article deals with the identification of gene regula-tory networks from experimental data using...
Method: The objective of the present article is to propose and evaluate a probabilistic approach bas...
Method: The objective of the present article is to propose and evaluate a probabilistic approach bas...
Recent advances in high-throughput molecular biology has motivated in the field of bioinformatics th...
Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemica...
Gene regulatory networks are collections of genes that interact, whether directly or indirectly, wit...
In this chapter we discuss the advantages of the use of probabilistic graphical models for modelling...
The two main aims of this thesis is initially to develop a new frequentist gene expression index (FO...
<div><p>We compare three state-of-the-art Bayesian inference methods for the estimation of the unkno...
We compare three state-of-the-art Bayesian inference methods for the estimation of the unknown param...
Computational systems biology is concerned with the development of detailed mechanistic models of bi...
Mathematical modelling opens the door to a rich pathway to study the dynamic properties of biologica...
Gene regulatory networks are a visual representation of genes and their interactions. In this visual...
In this paper we investigate Monte Carlo methods for the approximation of the posterior probability ...
In this paper, we apply Bayesian networks (BN) to infer gene regulatory network (GRN) model from gen...
This article deals with the identification of gene regula-tory networks from experimental data using...
Method: The objective of the present article is to propose and evaluate a probabilistic approach bas...
Method: The objective of the present article is to propose and evaluate a probabilistic approach bas...
Recent advances in high-throughput molecular biology has motivated in the field of bioinformatics th...
Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemica...
Gene regulatory networks are collections of genes that interact, whether directly or indirectly, wit...
In this chapter we discuss the advantages of the use of probabilistic graphical models for modelling...
The two main aims of this thesis is initially to develop a new frequentist gene expression index (FO...
<div><p>We compare three state-of-the-art Bayesian inference methods for the estimation of the unkno...
We compare three state-of-the-art Bayesian inference methods for the estimation of the unknown param...