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PREDICTION OF RESIDUE EXPOSURE AND CONTACT NUMBER FOR SIMPLIFIED HP LATTICE MODEL PROTEINS USING LEARNING CLASSIFIER SYSTEMS
2006
Applied Artificial Intelligence
The performance of a Learning Classifier System (LCS) applied to the classification of simplified hydrophobic/polar (HP) lattice model proteins was compared to other machine learning (ML) algorithms. The GAssist LCS classified functional HP model proteins on the 3D diamond lattice as folding or non-folding at 88.3% accuracy, outperforming significantly three out of the four other methods. GAssist correctly classified HP model protein instances on the basis of Contact Number (CN) and Residue
doi:10.1142/9789812774118_0085
fatcat:vhlm2n3w4rc6tp72g7me3femhy