We tackle the problem of completing and inferring genetic networks under stationary conditions from static data, where network\ncompletion is to make the minimum amount of modifications to an initial network so that the completed network is most consistent\nwith the expression data in which addition of edges and deletion of edges are basic modification operations. For this problem, we\npresent a new method for network completion using dynamic programming and least-squares fitting. This method can find an\noptimal solution in polynomial time if themaximumindegree of the network is bounded by a constant.We evaluate the effectiveness\nof our method through computational experiments using synthetic data. Furthermore, we demonstrate that our proposed method\ncan distinguish the differences between two types of genetic networks under stationary conditions from lung cancer and normal\ngene expression data.
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