RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Swarm intelligence
Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Parallel particle swarm optimization applied to the protein folding problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Protein structure prediction in lattice models with particle swarm optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Protein structure prediction using particle swarm optimization and a distributed parallel approach
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
Protein structure prediction using distributed parallel particle swarm optimization
Natural Computing: an international journal
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Computational Biology and Chemistry
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One of the main problems of computational approaches to protein structure prediction is the computational complexity. Many researches use simplified models to represent protein structure. Toy model is one of the simplification models. Finding the ground state is critical to the toy model of protein. This paper applies Particle Swarm Optimization (PSO) Algorithm to search the ground state of toy model for protein folding, and performs experiments both on artificial data and real protein data to evaluate the PSO-based method. The results show that on one hand, the PSO method is feasible and effective to search for ground state of toy model; on the other hand, toy model just can simulate real protein to some extent, and need further improvements.