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...
Main Authors: | , |
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Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2013-01-01
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Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3691143?pdf=render |