Add to ORKGMotivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological know-ledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP). BNP depicts the relation between various evidence types that contribute to the event ‘gene interaction ’ and is used to calculate the probability of a candidate graph (G) in the structure learning process. Results: Our simul...
Graphical modeling represents an established methodology for identifying complex dependencies in bio...
<p>A Bayesian network is a machine learning tool for organizing and encoding statistical dependence ...
Bayesian network techniques have been used for discovering causal relationships among large number o...
Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the ...
Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the ...
Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the ...
Motivation: Reverse engineering gene interaction networks from experimental data is a challenging ta...
MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative c...
Motivation: Network inference algorithms are powerful computational tools for identifying putative c...
Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior k...
Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior k...
Most of the approaches developed in the literature to elicit the a-priori distribution on Directed A...
In this work, we propose approaches for the inference of graphical models in the Bayesian framework....
In this work, we address both the computational and modeling aspects of Bayesian network structure ...
Inferring regulatory networks from experimental data via probabilistic graphical models is a popular...
Graphical modeling represents an established methodology for identifying complex dependencies in bio...
<p>A Bayesian network is a machine learning tool for organizing and encoding statistical dependence ...
Bayesian network techniques have been used for discovering causal relationships among large number o...
Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the ...
Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the ...
Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the ...
Motivation: Reverse engineering gene interaction networks from experimental data is a challenging ta...
MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative c...
Motivation: Network inference algorithms are powerful computational tools for identifying putative c...
Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior k...
Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior k...
Most of the approaches developed in the literature to elicit the a-priori distribution on Directed A...
In this work, we propose approaches for the inference of graphical models in the Bayesian framework....
In this work, we address both the computational and modeling aspects of Bayesian network structure ...
Inferring regulatory networks from experimental data via probabilistic graphical models is a popular...
Graphical modeling represents an established methodology for identifying complex dependencies in bio...
<p>A Bayesian network is a machine learning tool for organizing and encoding statistical dependence ...
Bayesian network techniques have been used for discovering causal relationships among large number o...