Elements of information theory
Elements of information theory
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
Multimeme Algorithms for Protein Structure Prediction
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Coordination number prediction using learning classifier systems: performance and interpretability
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Automated alphabet reduction method with evolutionary algorithms for protein structure prediction
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Fast rule representation for continuous attributes in genetics-based machine learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Prediction of topological contacts in proteins using learning classifier systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Evolving l-systems to capture protein structure native conformations
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
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A key open problem, which has defied scientists for decades is the problem of predicting the 3D structure of proteins (Protein Structure Prediction - PSP) based on its primary sequence: the amino acids that compose a protein chain. Full atomistic molecular dynamics simulations are, for all intents and purposes, impractical as current empirical models may require massive computational resources. One of the possible ways of alleviating this cost and making the problem easier is to simplify the protein representation based on which the native 3D state is searched for. We have proposed a protocol based on evolutionary algorithms to perform this simplification of the protein representation. Our protocol does not use any domain knowledge. Instead it uses a well known information theory metric, Mutual Information, to generate a reduced representation that is able to maintain the crucial information needed for PSP. The evaluation process of our method has shown that it generates alphabets that have competent performance against the original, non-simplified, representation. Moreover, these reduced alphabets obtain better-than-human performance when compared to some classic reduced alphabets.