Connections: the geometric bridge between art and science
Connections: the geometric bridge between art and science
Genetic algorithms in machine learning
Machine Learning and Its Applications
Blue Gene: a vision for protein science using a petaflop supercomputer
IBM Systems Journal - Deep computing for the life sciences
Biologically-implemented genetic algorithm for protein engineering
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Protein folding with cellular automata in the 3D HP model
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In this paper, a model based on genetic algorithms for protein folding prediction is proposed. The most important features of the proposed approach are: i) Heuristic secondary structure information is used in the initialization of the genetic algorithm; ii) An enhanced 3D spatial representation called cube-octahedron is used, also, an expansion technique is proposed in order to reduce the computational complexity and spatial constraints; iii) Data preprocessing of geometric features to characterize the cube-octahedron using twelve basic vectors to define the nodes. Additionally, biological information (torsion angles, bond angles and secondary structure conformations) was pre-processed through an analysis of all possible combinations of the basic vectors which satisfy the biological constrains defined by the spatial representation; and iv) Hashing techniques were used to improve the computational efficiency. The pre-processed information was stored in hash tables, which are intensively used by the genetic algorithm. Some experiments were carried out to validate the proposed model obtaining very promising results.