Signaling network prediction by the Ontology Fingerprint enhanced Bayesian network
dc.contributor.author | Qin, Tingting | |
dc.contributor.author | Tsoi, Lam C | |
dc.contributor.author | Sims, Kellie J | |
dc.contributor.author | Lu, Xinghua | |
dc.contributor.author | Zheng, W J | |
dc.date.accessioned | 2014-12-08T17:45:52Z | |
dc.date.available | 2014-12-08T17:45:52Z | |
dc.date.issued | 2012-12-17 | |
dc.identifier.citation | BMC Systems Biology. 2012 Dec 17;6(Suppl 3):S3 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/109490 | en_US |
dc.description.abstract | Abstract Background Despite large amounts of available genomic and proteomic data, predicting the structure and response of signaling networks is still a significant challenge. While statistical method such as Bayesian network has been explored to meet this challenge, employing existing biological knowledge for network prediction is difficult. The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses. Results We applied our novel approach to address the Predictive Signaling Network Modeling challenge of the fourth (2009) Dialog for Reverse Engineering Assessment's and Methods (DREAM4) competition. The challenge results showed that our method accurately captured signal transduction of a network of protein kinases and phosphoproteins in that the predicted protein phosphorylation levels under all experimental conditions were highly correlated (R2 = 0.93) with the observed results. Based on the evaluation of the DREAM4 organizer, our team was ranked as one of the top five best performers in predicting network structure and protein phosphorylation activity under test conditions. Conclusions Bayesian network can be used to simulate the propagation of signals in cellular systems. Incorporating the Ontology Fingerprint as prior biological knowledge allows us to efficiently infer concise signaling network structure and to accurately predict cellular responses. | |
dc.title | Signaling network prediction by the Ontology Fingerprint enhanced Bayesian network | |
dc.type | Article | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/109490/1/12918_2012_Article_989.pdf | |
dc.identifier.doi | 10.1186/1752-0509-6-S3-S3 | en_US |
dc.language.rfc3066 | en | |
dc.rights.holder | Qin et al.; licensee BioMed Central Ltd. | |
dc.date.updated | 2014-12-08T17:45:53Z | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.