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Learning robust cell signalling models from high throughput proteomic data
We propose a framework for learning robust Bayesian network models of cell signalling from high-throughput proteomic data. We show that model averaging using Bayesian bootstrap resampling generates more robust structures than procedures that learn structures using all of the data. We also develop an...
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| Pubblicato in: | Int J Bioinform Res Appl |
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| Autori principali: | , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2009
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4292923/ https://ncbi.nlm.nih.gov/pubmed/19525198 |
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