Abstract
BackgroundDespite large amounts of available genomic and proteomic data, predicting the structure and response of signaling networks is still a significant challenge. While statistical method such as Bayesian network has been explored to meet this challenge, employing existing biological knowledge for network prediction is difficult. The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses.ResultsWe applied our novel approach to address the Predictive Signaling Network Modeling challenge of the fourth (2009) Dialog for Reverse Engineering Assessment's and Methods (DREAM4) competition. The challenge results showed that our method accurately captured signal transduction of a network of protein kinases and phosphoproteins in that the predicted protein phosphorylation levels under all experimental conditions were highly correlated (R2 = 0.93) with the observed results. Based on the evaluation of the DREAM4 organizer, our team was ranked as one of the top five best performers in predicting network structure and protein phosphorylation activity under test conditions.ConclusionsBayesian network can be used to simulate the propagation of signals in cellular systems. Incorporating the Ontology Fingerprint as prior biological knowledge allows us to efficiently infer concise signaling network structure and to accurately predict cellular responses.
Highlights
Despite large amounts of available genomic and proteomic data, predicting the structure and response of signaling networks is still a significant challenge
We recently developed the concept of the Ontology Fingerprint from biomedical literature and Gene Ontology (GO) [32]
We addressed the task of learning network structure through combining prior knowledge and experimental data in the following steps: 1) stochastically exploring candidate network structures based on prior knowledge; 2) training candidate Bayesian network using experimental data, which further modifies network structure through parameterization, i.e., setting the parameters associated with certain edges to the values that would be equivalent to deleting these edges; and 3) selecting the network model that best simulates the experimental results
Summary
Despite large amounts of available genomic and proteomic data, predicting the structure and response of signaling networks is still a significant challenge. While statistical method such as Bayesian network has been explored to meet this challenge, employing existing biological knowledge for network prediction is difficult. The objective of this study is to develop a novel approach that integrates prior biological knowledge in the form of the Ontology Fingerprint to infer cell-type-specific signaling networks via data-driven Bayesian network learning; and to further use the trained model to predict cellular responses. Bayesian network, which can explicitly handle the uncertainty of unobserved events [14,17], provides a compact graphical representation of the joint probability distributions over all random variables, and has been used for reconstruction of signaling networks [18,19,20,21,22,23,24]
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