Scutari, M;
Nagarajan, R;
(2013)
Identifying significant edges in graphical models of molecular networks.
Artif Intell Med
, 57
(3)
207 - 217.
10.1016/j.artmed.2012.12.006.
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Abstract
Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graphical models and networks have especially proven to be useful abstractions in this regard. Ad hoc thresholds are often used in conjunction with structure learning algorithms to determine significant associations. The present study overcomes this limitation by proposing a statistically motivated approach for identifying significant associations in a network.
| Type: | Article |
|---|---|
| Title: | Identifying significant edges in graphical models of molecular networks. |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.artmed.2012.12.006 |
| Publisher version: | http://dx.doi.org/10.1016/j.artmed.2012.12.006 |
| Language: | English |
| Additional information: | © 2012 Elsevier B.V. Open access under CC BY license. PMCID: PMC4070079 |
| Keywords: | Algorithms, Computer Graphics, Flow Cytometry, Models, Theoretical, Proteins, Signal Transduction |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1376999 |
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