AbstractWe define a new class of coloured graphical models, called regulatory graphs. These graphs have their own distinctive formal semantics and can directly represent typical qualitative hypotheses about regulatory processes like those described by various biological mechanisms. They admit an embellishment into classes of probabilistic statistical models and so standard Bayesian methods of model selection can be used to choose promising candidate explanations of regulation. Regulation is modelled by the existence of a deterministic relationship between the longitudinal series of observations labelled by the receiving vertex and the donating one. This class contains longitudinal cluster models as a degenerate graph. Edge colours directly ...
In this work, we propose approaches for the inference of graphical models in the Bayesian framework....
We develop a method for reconstructing regulatory interconnection networks between variables evolvin...
In this chapter we discuss the advantages of the use of probabilistic graphical models for modelling...
We de ne a new class of coloured graphical models, called regulatory graphs. These graphs have thei...
Gene regulatory networks are a visual representation of genes and their interactions. In this visual...
Differential equations have been established to model the dynamic behavior of gene regulatory networ...
Differential networks allow us to better understand the changes in cellular processes that are exhib...
Method: The objective of the present article is to propose and evaluate a probabilistic approach bas...
Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene r...
Method: The objective of the present article is to propose and evaluate a probabilistic approach bas...
Contains fulltext : 197601.pdf (publisher's version ) (Open Access)International C...
AbstractThis paper introduces two new probabilistic graphical models for reconstruction of genetic r...
We propose a model-driven approach for analyzing genomic expression data that permits genetic regula...
Inference about regulatory networks from high-throughput genomics data is of great interest in syste...
The inference of gene regulatory networks (GRN) from microarrray data suffers from the low accuracy ...
In this work, we propose approaches for the inference of graphical models in the Bayesian framework....
We develop a method for reconstructing regulatory interconnection networks between variables evolvin...
In this chapter we discuss the advantages of the use of probabilistic graphical models for modelling...
We de ne a new class of coloured graphical models, called regulatory graphs. These graphs have thei...
Gene regulatory networks are a visual representation of genes and their interactions. In this visual...
Differential equations have been established to model the dynamic behavior of gene regulatory networ...
Differential networks allow us to better understand the changes in cellular processes that are exhib...
Method: The objective of the present article is to propose and evaluate a probabilistic approach bas...
Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene r...
Method: The objective of the present article is to propose and evaluate a probabilistic approach bas...
Contains fulltext : 197601.pdf (publisher's version ) (Open Access)International C...
AbstractThis paper introduces two new probabilistic graphical models for reconstruction of genetic r...
We propose a model-driven approach for analyzing genomic expression data that permits genetic regula...
Inference about regulatory networks from high-throughput genomics data is of great interest in syste...
The inference of gene regulatory networks (GRN) from microarrray data suffers from the low accuracy ...
In this work, we propose approaches for the inference of graphical models in the Bayesian framework....
We develop a method for reconstructing regulatory interconnection networks between variables evolvin...
In this chapter we discuss the advantages of the use of probabilistic graphical models for modelling...