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Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support Vector Machines
2009
2009 IEEE International Conference on Bioinformatics and Biomedicine
Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using features extracted from only protein sequence with support vector machines. On a standard benchmark dataset, the accuracy of folding kinetic type classification is 80%. The Pearson correlation coefficient and the mean absolute difference
doi:10.1109/bibm.2009.21
dblp:conf/bibm/LinWXC09
fatcat:cmw2trfpbzattgbee45hfbrlly