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We describe a new approach for combining and comparing: (1) The information that gene expression measurements represent. (2) Prior biological knowledge (that is modeled as a weighted graph). Our approach includes translating the prior biological knowledge to a Bayesian Network (BN), and searching solutions with a small edit distance from that BN but with a significant better fitness to the gene expression measurements. This method can be useful for analyzing gene expression of patients whose regulatory processes are slightly different than those that are known from the literature (e.g. for healthy subjects).We demonstrate the viability of our method by analyzing synthetic and biological examples.