A novel approach to phylogenetic tree construction using stochastic optimization and clustering.
BACKGROUND
The problem of inferring the evolutionary history and constructing the phylogenetic tree with high performance has become one of the major problems in computational biology.


RESULTS
A new phylogenetic tree construction method from a given set of objects (proteins, species, etc.) is presented. As an extension of ant colony optimization, this method proposes an adaptive phylogenetic clustering algorithm based on a digraph to find a tree structure that defines the ancestral relationships among the given objects.


CONCLUSIONS
Our phylogenetic tree construction method is tested to compare its results with that of the genetic algorithm (GA). Experimental results show that our algorithm converges much faster and also achieves higher quality than GA.
Qin
Ling
L
Department of Computer Science, Nanjing University of Aeronautics and Astronautics, Nanjing, 210096, China. qllynne@nuaa.edu.cn
Chen
Yixin
Y
Pan
Yi
Y
Chen
Ling
L
D000465:Algorithms; D000818:Animals; D001000:Ants / Q000502:physiology*; D001522:Behavior, Animal / Q000502:physiology; D005075:Biological Evolution; D032701:Biomimetics / Q000379:methods*; D016000:Cluster Analysis; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D010802:Phylogeny; D012919:Social Behavior; D013269:Stochastic Processes