Bayesian network inference of phosphoproteomic signaling networks
نویسندگان
چکیده
Machine learning techniques are becoming increasingly useful in the study of complex biological phenomena. As more is understood about biological regulation, and additional experimental methods are developed, it is also becoming feasible to attempt to “reverse engineer” the pathways regulating biological systems using graphical models, with an emphasis on Bayesian networks. Herein we describe the background of Bayesian networks being applied to biological networks, apply Bayesian network inference to five-node differential equation models of two cellular networks, and discuss issues moving forward with the application of Bayesian networks to modeling phosphoproteomic data.
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