1,094 research outputs found
Minimal Phylogenetic Supertrees and Local Consensus Trees
The problem of constructing a minimally resolved phylogenetic supertree (i.e., having the smallest possible number of internal nodes) that contains all of the rooted triplets from a consistent set R is known to be NP-hard. In this paper, we prove that constructing a phylogenetic tree consistent with R that contains the minimum number of additional rooted triplets is also NP-hard, and develop exact, exponential-time algorithms for both problems. The new algorithms are applied to construct two variants of the local consensus tree;
for any set S of phylogenetic trees over some leaf label set L,
this gives a minimal phylogenetic tree over L that contains every
rooted triplet present in all trees in S, where ``minimal\u27\u27 means either having the smallest possible number of internal nodes or
the smallest possible number of rooted triplets. The second variant generalizes the RV-II tree, introduced by Kannan, Warnow, and Yooseph in 1998
Fixed Parameter Polynomial Time Algorithms for Maximum Agreement and Compatible Supertrees
Consider a set of labels and a set of trees {\mathcal T} = \{{\mathcal
T}^{(1), {\mathcal T}^{(2), ..., {\mathcal T}^{(k) \$ where each tree
{\mathcal T}^{(i)L\mathcal T}{\mathcal T}k \geq 3kD$
of the trees are constant
On Finding the Adams Consensus Tree
This paper presents a fast algorithm for finding the Adams consensus tree of a set of conflicting phylogenetic trees with identical leaf labels, for the first time improving the time complexity of a widely used algorithm invented by Adams in 1972 [1]. Our algorithm applies
the centroid path decomposition technique [9] in a new way to traverse the input trees\u27 centroid paths in unison, and runs in O(k n log n) time, where k is the number of input trees and n is the size of the leaf label set. (In comparison, the old algorithm from 1972 has a worst-case running time of O(k n^2).) For the special case of k = 2, an even faster algorithm running in O(n cdot frac{log n}{loglog n}) time is provided, which relies on an extension of the wavelet tree-based technique by Bose et al. [6] for orthogonal range counting on a grid.
Our extended wavelet tree data structure also supports truncated
range maximum queries efficiently and may be of independent interest to algorithm designers
A Decomposition Theorem for Maximum Weight Bipartite Matchings
Let G be a bipartite graph with positive integer weights on the edges and
without isolated nodes. Let n, N and W be the node count, the largest edge
weight and the total weight of G. Let k(x,y) be log(x)/log(x^2/y). We present a
new decomposition theorem for maximum weight bipartite matchings and use it to
design an O(sqrt(n)W/k(n,W/N))-time algorithm for computing a maximum weight
matching of G. This algorithm bridges a long-standing gap between the best
known time complexity of computing a maximum weight matching and that of
computing a maximum cardinality matching. Given G and a maximum weight matching
of G, we can further compute the weight of a maximum weight matching of G-{u}
for all nodes u in O(W) time.Comment: The journal version will appear in SIAM Journal on Computing. The
conference version appeared in ESA 199
An Even Faster and More Unifying Algorithm for Comparing Trees via Unbalanced Bipartite Matchings
A widely used method for determining the similarity of two labeled trees is
to compute a maximum agreement subtree of the two trees. Previous work on this
similarity measure is only concerned with the comparison of labeled trees of
two special kinds, namely, uniformly labeled trees (i.e., trees with all their
nodes labeled by the same symbol) and evolutionary trees (i.e., leaf-labeled
trees with distinct symbols for distinct leaves). This paper presents an
algorithm for comparing trees that are labeled in an arbitrary manner. In
addition to this generality, this algorithm is faster than the previous
algorithms.
Another contribution of this paper is on maximum weight bipartite matchings.
We show how to speed up the best known matching algorithms when the input
graphs are node-unbalanced or weight-unbalanced. Based on these enhancements,
we obtain an efficient algorithm for a new matching problem called the
hierarchical bipartite matching problem, which is at the core of our maximum
agreement subtree algorithm.Comment: To appear in Journal of Algorithm
Cavity Matchings, Label Compressions, and Unrooted Evolutionary Trees
We present an algorithm for computing a maximum agreement subtree of two
unrooted evolutionary trees. It takes O(n^{1.5} log n) time for trees with
unbounded degrees, matching the best known time complexity for the rooted case.
Our algorithm allows the input trees to be mixed trees, i.e., trees that may
contain directed and undirected edges at the same time. Our algorithm adopts a
recursive strategy exploiting a technique called label compression. The
backbone of this technique is an algorithm that computes the maximum weight
matchings over many subgraphs of a bipartite graph as fast as it takes to
compute a single matching
Using indirect protein interactions for the prediction of Gene Ontology functions
10.1186/1471-2105-8-S4-S8BMC Bioinformatics8SUPPL. 4BBMI
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