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Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources
Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior k...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2013
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3691143/ https://www.ncbi.nlm.nih.gov/pubmed/23826291 http://dx.doi.org/10.1371/journal.pone.0067410 |