HIGHLIGHTS
- who: December and colleagues from the Johns Hopkins University, United States have published the paper: Inference of regulatory networks through temporally sparse data, in the Journal: (JOURNAL)
- what: The authors develop a method that is scalable with respect to the number of unknown interactions, and efficiently searches over the large topology candidate space. When the optimization ends, the topology with the largest evaluated likelihood value is selected as the system topology, meaning that: surrogate model will be reduced as the authors evaluate the likelihood function for more topologies. Using the proposed method, the authors show . . .
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