Details in the related publication: A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology Marco Grzegorczyk / Dirk Husmeier DOI: https://doi.org/10.1515/1544-6115.176
Background: A central goal of molecular biology is to understand the regulatory mechanisms of gene t...
The focus of this PhD thesis has been on two well-known and widely applied statistical model classes...
In statistical genomics and systems biology non-homogeneous dynamic Bayesian networks (NH-DBNs) have...
Details in the related publication: A Non-Homogeneous Dynamic Bayesian Network with Sequentially Cou...
<p>The data stem from</p><p>I. Cantone, L. Marucci, F. Iorio, M.A. Ricci, V. Belcastro, M. Bansal, S...
Motivation: Non-homogeneous dynamic Bayesian networks (NH-DBNs) are a popular tool for learning netw...
In systems biology, nonhomogeneous dynamic Bayesian networks (NH-DBNs) have become a popular modelin...
An important and challenging problem in systems biology is the inference of gene regulatory networks...
An important and challenging problem in systems biology is the inference of gene regulatory networks...
10.1007/978-3-642-33386-6_2Lecture Notes in Computer Science (including subseries Lecture Notes in A...
Motivation: Network inference algorithms are powerful computational tools for identifying putative c...
also at ple Available data sources include static steady state data and time course data obtained ei...
Abstract High-throughput data acquisition in synthetic biology leads to an abundance of data that n...
The central question of systems biology is to understand how individual components of a biological s...
To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent st...
Background: A central goal of molecular biology is to understand the regulatory mechanisms of gene t...
The focus of this PhD thesis has been on two well-known and widely applied statistical model classes...
In statistical genomics and systems biology non-homogeneous dynamic Bayesian networks (NH-DBNs) have...
Details in the related publication: A Non-Homogeneous Dynamic Bayesian Network with Sequentially Cou...
<p>The data stem from</p><p>I. Cantone, L. Marucci, F. Iorio, M.A. Ricci, V. Belcastro, M. Bansal, S...
Motivation: Non-homogeneous dynamic Bayesian networks (NH-DBNs) are a popular tool for learning netw...
In systems biology, nonhomogeneous dynamic Bayesian networks (NH-DBNs) have become a popular modelin...
An important and challenging problem in systems biology is the inference of gene regulatory networks...
An important and challenging problem in systems biology is the inference of gene regulatory networks...
10.1007/978-3-642-33386-6_2Lecture Notes in Computer Science (including subseries Lecture Notes in A...
Motivation: Network inference algorithms are powerful computational tools for identifying putative c...
also at ple Available data sources include static steady state data and time course data obtained ei...
Abstract High-throughput data acquisition in synthetic biology leads to an abundance of data that n...
The central question of systems biology is to understand how individual components of a biological s...
To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent st...
Background: A central goal of molecular biology is to understand the regulatory mechanisms of gene t...
The focus of this PhD thesis has been on two well-known and widely applied statistical model classes...
In statistical genomics and systems biology non-homogeneous dynamic Bayesian networks (NH-DBNs) have...
DOI
Add to ORKG