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Description:
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Recently , the stochastic approximation Monte Carlo algorithm has been proposed
by Liang et al . (2005 ) as a general -purpose stochastic optimization and simulation
algorithm . An annealing version of this algorithm was developed for real small protein folding problems . The numerical results indicate that it outperforms simulated
annealing and conventional Monte Carlo algorithms as a stochastic optimization algorithm . We also propose one method for the use of secondary structures in protein
folding . The predicted protein structures are rather close to the true structures .
Phylogenetic trees have been used in biology for a long time to graphically represent evolutionary relationships among species and genes . An understanding of evolutionary relationships is critical to appropriate interpretation of bioinformatics results .
The use of the sequential structure of phylogenetic trees in conjunction with stochastic approximation Monte Carlo was developed for phylogenetic tree reconstruction .
The numerical results indicate that it has a capability of escaping from local traps
and achieving a much faster convergence to the global likelihood maxima than other phylogenetic tree reconstruction methods , such as BAMBE and MrBayes . |