Bayesian networks are increasingly important for integrating biological data and for inferring cellular networks and pathways. What are Bayesian networks and how are they used for inference?
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fnbt0106-51/MediaObjects/41587_2006_Article_BFnbt010651_Fig1_HTML.gif)
Bob Crimi
References
Eddy, S.R. What is Bayesian statistics? Nat. Biotechnol. 22, 1177–1178, 2004.
Heckerman, D. A tutorial on learning with Bayesian networks. in Learning in Graphical Models (ed. Jordan, M.I.) 301–354 (Kluwer Academic, Boston 1998).
Husmeier, D., Dybowski, R. & Roberts, S (eds.). Probabilistic Modeling in Bioinformatics and Medical Informatics (Springer, London 2005).
Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799–805, 2004.
Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D.A. & Nolan, G.P. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523–529, 2005.
Beaumont, M.A. & Rannala, B. The Bayesian revolution in genetics. Nat. Rev. Genet. 5, 251–261, 2004.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Needham, C., Bradford, J., Bulpitt, A. et al. Inference in Bayesian networks. Nat Biotechnol 24, 51–53 (2006). https://doi.org/10.1038/nbt0106-51
Issue Date:
DOI: https://doi.org/10.1038/nbt0106-51
This article is cited by
-
An efficient Bayesian network model (BNM) for software risk prediction in design phase development
International Journal of Information Technology (2023)
-
A single shot coherent Ising machine based on a network of injection-locked multicore fiber lasers
Nature Communications (2019)
-
Nonlinear optical components for all-optical probabilistic graphical model
Nature Communications (2018)
-
Enhanced construction of gene regulatory networks using hub gene information
BMC Bioinformatics (2017)
-
Uncovering distinct protein-network topologies in heterogeneous cell populations
BMC Systems Biology (2015)