Cell-Type-Specific Predictive Network Yields Novel Insights into Mouse Embryonic Stem Cell Self-Renewal and Cell Fate
A Bayesian network is a machine learning tool for organizing and encoding statistical dependence relationships among pieces of knowledge. A naïve Bayesian network is a simplified version of a Bayesian network in which all child nodes are dependent on the parent and independent of each other. This type of graphical device may be used to combine different types of evidential data and prior knowledge to generate probabilistic models of biological functional relationship networks. In our naïve Bayes net structure, the functional relationship between the pair of proteins i and j (FRij) is a hidden conditional variable (indicating the unknown or hidden probability that these two gene products are functionally associated), on which all dataset evidence variables are dependent, and represents the discretized, observed similarity score in dataset k for proteins i and j. The edge weight (eij) represents the probability that the proteins ij are functionally related given the evidence observed in different high-throughput datasets. Strong evidence of a functional relationship between protein pairs, measured by edge weight, indicates the proteins behave in a similar way given observed patterns in the high-throughput data. The specific nature of that relationship can be deduced by evaluating the type of datasets that contribute to that edge weight, followed experimental validation.