C4.5: programs for machine learning
C4.5: programs for machine learning
Protein Structure from Contact Maps: A Case-Based Reasoning Approach
Information Systems Frontiers
Protein Contact Map Prediction Based on an Ensemble Learning Method
ICCET '09 Proceedings of the 2009 International Conference on Computer Engineering and Technology - Volume 02
Pattern Recognition Letters
An Integrated Approach for Protein Structure Prediction Using Artificial Neural Network
ICCEA '10 Proceedings of the 2010 Second International Conference on Computer Engineering and Applications - Volume 02
A decision tree-based method for protein contact map prediction
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
DTP: decision tree-based predictor of protein contact map
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Mining of protein contact maps for protein fold prediction
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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In this paper, we focus on protein contact map prediction, one of the most important intermediate steps of the protein folding problem. The objective of this research is to know how short-range interactions can contribute to a system based on decision trees to learn about the correlation among the covalent structures of a protein residues. We propose a solution to predict protein contact maps that combines the use of decision trees with a new input codification for short-range interactions. The method's performance was very satisfactory, improving the accuracy instead using all information of the protein sequence. For a globulin data set the method can predict contacts with a maximal accuracy of 43%. The presented predictive model illustrates that short-range interactions play the predominant role in determining protein structure.