In the present contribution we provide a discussion of the paper on ‘‘Bayesian graphical models for modern biological applications’’. The authors present an extensive review of Bayesian graphical models, which are used for a variety of inferential tasks applied to biology and medicine settings. Our contribution proposes a conceptual connection between two scientific frameworks, graphical models and social network analysis, by highlighting also the role played by network models and random graphs. A bibliometric analysis is performed by exploiting publications collected from online bibliographic archives to map the main themes characterizing the two research fields. Specifically, a co-word network analysis is carried out using visual...
Beretta, S., Castelli, M., Gonçalves, I., Merelli, I., & Ramazzotti, D. (2016). Combining Bayesian a...
This chapter introduces a probabilistic approach to modelling in physiology and medicine: the quanti...
<p>Motivation: An important problem in systems biology is the inference of biochemical pathway...
In the present contribution we provide a discussion of the paper on ‘‘Bayesian graphical models for...
In the present contribution we provide a discussion of the paper on “Bayesian graphical models for m...
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathem...
In this work, we propose approaches for the inference of graphical models in the Bayesian framework....
In this chapter we discuss the advantages of the use of probabilistic graphical models for modelling...
An important problem in systems biology is to infer the architecture of gene regulatory networks and...
Graphical modelling in its modern form was pioneered by Lauritzen and Wer-muth [43] and Pearl [55] i...
Thesis (Ph.D.)--University of Washington, 2013The advent of high-dimensional biological data from te...
Mathematical problems such as graph theory problems are of increasing importance for the analysis of...
The rise of network data in many different domains has offered researchers new insight into the prob...
Gene regulatory networks are a visual representation of genes and their interactions. In this visual...
Solidly defined and comprehensive graphical representation of biological networks is essential for e...
Beretta, S., Castelli, M., Gonçalves, I., Merelli, I., & Ramazzotti, D. (2016). Combining Bayesian a...
This chapter introduces a probabilistic approach to modelling in physiology and medicine: the quanti...
<p>Motivation: An important problem in systems biology is the inference of biochemical pathway...
In the present contribution we provide a discussion of the paper on ‘‘Bayesian graphical models for...
In the present contribution we provide a discussion of the paper on “Bayesian graphical models for m...
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathem...
In this work, we propose approaches for the inference of graphical models in the Bayesian framework....
In this chapter we discuss the advantages of the use of probabilistic graphical models for modelling...
An important problem in systems biology is to infer the architecture of gene regulatory networks and...
Graphical modelling in its modern form was pioneered by Lauritzen and Wer-muth [43] and Pearl [55] i...
Thesis (Ph.D.)--University of Washington, 2013The advent of high-dimensional biological data from te...
Mathematical problems such as graph theory problems are of increasing importance for the analysis of...
The rise of network data in many different domains has offered researchers new insight into the prob...
Gene regulatory networks are a visual representation of genes and their interactions. In this visual...
Solidly defined and comprehensive graphical representation of biological networks is essential for e...
Beretta, S., Castelli, M., Gonçalves, I., Merelli, I., & Ramazzotti, D. (2016). Combining Bayesian a...
This chapter introduces a probabilistic approach to modelling in physiology and medicine: the quanti...
<p>Motivation: An important problem in systems biology is the inference of biochemical pathway...