An Algorithm for Constructing Local Regions in a Phylogenetic Network
โ Scribed by K.T. Huber; E.E. Watson; M.D. Hendy
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 102 KB
- Volume
- 19
- Category
- Article
- ISSN
- 1055-7903
No coin nor oath required. For personal study only.
โฆ Synopsis
The groupings of taxa in a phylogenetic tree cannot represent all the conflicting signals that usually occur among site patterns in aligned homologous genetic sequences. Hence a tree-building program must compromise by reporting a subset of the patterns, using some discriminatory criterion. Thus, in the worst case, out of possibly a large number of equally good trees, only an arbitrarily chosen tree might be reported by the tree-building program as "The Tree." This tree might then be used as a basis for phylogenetic conclusions. One strategy to represent conflicting patterns in the data is to construct a network. The Buneman graph is a theoretically very attractive example of such a network. In particular, a characterization for when this network will be a tree is known. Also the Buneman graph contains each of the most parsimonious trees indicated by the data. In this paper we describe a new method for constructing the Buneman graph that can be used for a generalization of Hadamard conjugation to networks. This new method differs from previous methods by allowing us to focus on local regions of the graph without having to first construct the full graph. The construction is illustrated by an example.
๐ SIMILAR VOLUMES
In this paper, we propose a neural network algorithm that uses the expanded maximum neuron model to solve the channel assignment problem of cellular radio networks, which is an NP-complete combinatorial optimization problem. The channel assignment problem demands minimizing the total interference be
In this paper, we deal with a network design problem arising from the deployment of synchronous optical networks (SONET), a standard of transmission using optical fiber technology. The problem is to find an optimal clustering of traffic demands in the network such that the total number of node assig