Modelling Nonstationary Gene Regulatory Processes

@inproceedings{Grzegorczyk2010ModellingNG,
  title={Modelling Nonstationary Gene Regulatory Processes},
  author={Marco Grzegorczyk and Dirk Husmeier and J{\"o}rg Rahnenf{\"u}hrer},
  booktitle={Adv. Bioinformatics},
  year={2010}
}
An important objective in systems biology is to infer gene regulatory networks from postgenomic data, and dynamic Bayesian networks have been widely applied as a popular tool to this end. The standard approach for nondiscretised data is restricted to a linear model and a homogeneous Markov chain. Recently, various generalisations based on changepoint processes and free allocation mixture models have been proposed. The former aim to relax the homogeneity assumption, whereas the latter are more… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 11 extracted citations

Yue Pan's PhD Thesis

View 4 Excerpts
Highly Influenced

Circadian systems biology: When time matters

Computational and structural biotechnology journal • 2015

References

Publications referenced by this paper.
Showing 1-10 of 27 references

Learning Gaussian Networks

View 5 Excerpts
Highly Influenced

Analyse de processus stochastiques pour la génomique : étude du modèle MTD et inférence de réseaux bayésiens dynamiques

S. Lèbre
Ph.D. thesis, Université d’Evry-Val-d’Essonne, • 2008
View 1 Excerpt

Similar Papers

Loading similar papers…