An Extreme Learning Machine Classifier for Prediction of Relative Solvent Accessibility in Proteins

A neural network based method called Sparse-Extreme Learning Machine (S-ELM) is used for prediction of Relative Solvent Accessibility (RSA) in proteins. We have shown that multiple-fold gains in speed of processing by S-ELM compared to using SVM for classification, while accuracy efficiencies are comparable to literature. The study indicates that using S-ELM would give a distinct advantage in terms of processing speed and performance for RSA prediction.

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