Proceedings of the 10th annual conference companion 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
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System
Learning Classifier Systems
A tale of human-competitiveness in bioinformatics
ACM SIGEVOlution
A mixed discrete-continuous attribute list representation for large scale classification domains
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolutionary design of the energy function for protein structure prediction
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
GP challenge: evolving energy function for protein structure prediction
Genetic Programming and Evolvable Machines
Speeding up the evaluation of evolutionary learning systems using GPGPUs
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Analysing bioHEL using challenging boolean functions
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Self-adaptation of parameters in a learning classifier system ensemble machine
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
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Motivation: We introduce a new method for designating the location of residues in folded protein structures based on the recursive convex hull (RCH) of a point set of atomic coordinates. The RCH can be calculated with an efficient and parameterless algorithm. Results: We show that residue RCH class contains information complementary to widely studied measures such as solvent accessibility (SA), residue depth (RD) and to the distance of residues from the centroid of the chain, the residues’ exposure (Exp). RCH is more conserved for related structures across folds and correlates better with changes in thermal stability of mutants than the other measures. Further, we assess the predictability of these measures using three types of machine-learning technique: decision trees (C4.5), Naive Bayes and Learning Classifier Systems (LCS) showing that RCH is more easily predicted than the other measures. As an exemplar application of predicted RCH class (in combination with other measures), we show that RCH is potentially helpful in improving prediction of residue contact numbers (CN). Contact: nxk@cs.nott.ac.uk Supplementary Information: For Supplementary data please refer to Datasets: www.infobiotic.net/datasets, RCH Prediction Servers: www.infobiotic.net