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Identifying significant edges in graphical models of molecular networks.

Abstract:

OBJECTIVE: 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 identifyi...

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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Role:
Author
Journal:
Artificial intelligence in medicine
Volume:
57
Issue:
3
Pages:
207-217
Publication date:
2013-03-05
DOI:
EISSN:
1873-2860
ISSN:
0933-3657
URN:
uuid:6585a07a-b5bd-4470-bbec-6698a798da8a
Source identifiers:
487492
Local pid:
pubs:487492

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