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Dynamic Bayesian network (DBN) methods have shown great promise in regulatory network reconstruction because of their capability of modeling causality and cyclic networks, and handling data with noises found in biological experiments. However, they tend to produce relative high false positives and are not computationally efficient even for networks of moderate size. This paper presents a novel DBN-based...
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