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JACM
2006
99views more  JACM 2006»
13 years 10 months ago
Finding a maximum likelihood tree is hard
Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
Benny Chor, Tamir Tuller
BMCBI
2005
100views more  BMCBI 2005»
13 years 9 months ago
EvDTree: structure-dependent substitution profiles based on decision tree classification of 3D environments
Background: Structure-dependent substitution matrices increase the accuracy of sequence alignments when the 3D structure of one sequence is known, and are successful e.g. in fold ...
Jean-Christophe Gelly, Laurent Chiche, Jér&...
PODC
2009
ACM
14 years 10 months ago
Brief announcement: minimum spanning trees and cone-based topology control
Consider a setting where nodes can vary their transmission power thereby changing the network topology, the goal of topology control is to reduce the transmission power while ensu...
Alejandro Cornejo, Nancy A. Lynch
BMCBI
2010
83views more  BMCBI 2010»
13 years 10 months ago
Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins
Background: Phylogenies capture the evolutionary ancestry linking extant species. Correlations and similarities among a set of species are mediated by and need to be understood in...
William P. Kelly, Michael P. H. Stumpf
ICML
1999
IEEE
14 years 10 months ago
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees
Lbr is a lazy semi-naive Bayesian classi er learning technique, designed to alleviate the attribute interdependence problem of naive Bayesian classi cation. To classify a test exa...
Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting