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2. Infer the edges in the ancestral networks once we have the genes of these networks.
In the first phase, we infer ancestral networks for the phylogeny (strings labelling internal nodes), using our own adaptation of the FastML[ 12] algorithm; in the second phase, these ancestral networks are used to refine the leaf networks.
We observe that our parsimony-based approach obtains high precision and recall, even on fairly distant ancestral networks.
Reconstructing plausible ancestral networks can help answer many natural questions about how present-day networks have evolved.
Further, putative ancestral networks can be used to help solve other difficult problems in computational biology, such as network alignment.
Finally, we show that our method accurately reconstructs a number of ancestral networks for the bZIP family of proteins.
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We examine how different transcriptional network structures can evolve from an ancestral network.
This network includes only about 5.5% of the ancestral network links.
21 Secondly, in our ignorance of ancestral network states, evolutionary reconstruction is a delicate exercise.
Our parsimony-based approach to ancestral network reconstruction is both efficient and accurate.
To evaluate the ancestral network reconstruction task, we use the bZIP family of proteins.
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