Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Proceedings of the sixth annual international conference on Computational biology
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Multimeme Algorithms for Protein Structure Prediction
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
The metabolic algorithm for P systems: Principles and applications
Theoretical Computer Science
A hybrid immune algorithm with information gain for the graph coloring problem
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Clonal selection algorithms: a comparative case study using effective mutation potentials
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
WMC'09 Proceedings of the 10th international conference on Membrane Computing
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An Immune Algorithm for Protein Structure Prediction on Lattice Models
IEEE Transactions on Evolutionary Computation
Off-lattice protein structure prediction with homologous crossover
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Multi-Objective Stochastic Search for Sampling Local Minima in the Protein Energy Surface
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Protein structure prediction problem: formalization using quaternions
Cybernetics and Systems Analysis
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Discrete models for protein structure prediction embed the protein amino acid sequence into a discrete spatial structure, usually a lattice, where an optimal tertiary structure is predicted on the basis of simple assumptions relating to the hydrophobic---hydrophilic character of amino acids in the sequence and to relevant interactions for free energy minimization. While the prediction problem is known to be NP complete even in the simple setting of Dill's model with a 2D-lattice, a variety of bio-inspired algorithms for this problem have been proposed in the literature. Immunological algorithms are inspired by the kind of optimization that immune systems perform when identifying and promoting the replication of the most effective antibodies against given antigens. A quick, state-of-the-art survey of discrete models and immunological algorithms for protein structure prediction is presented in this paper, and the main design and performance features of an immunological algorithm for this problem are illustrated in a tutorial fashion.