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
Improving the prediction of helix-residue contacts in all-alpha proteins
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
A branch-and-reduce algorithm for the contact map overlap problem
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
Protein contact map prediction using committee machine approach
International Journal of Data Mining and Bioinformatics
Predicting helix pair structure from fuzzy contact maps
Applied Soft Computing
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Contact map prediction is of great interest for its application in fold recognition and protein 3D structure determination. In this paper we present a contact-map prediction algorithm that employs Support Vector Machines as the machine learning tool and incorporates various features such as sequence profiles and their conservation, correlated mutation analysis based on various amino acid physicochemical properties, and secondary structure. In addition, weevaluated the effectivenes of the different features on contact map prediction for different fold classes. On average, our predictor achieved a prediction accuracy of 0 .2238 with an improvement over a random predictor of a factor 11.7 , which is better than reported studies. Our study showed that predicted secondary structure features play an important role for the protein containing beta structure. Models based on secondary structure feature and CMA features produce different sets of predictions. Our study also suggests that models learned separately for different protein fold families may achieve better performance than a unified model.