Kernel Risk Sensitive Loss-based Echo State Networks for Predicting Therapeutic Peptides with Sparse Learning

The detection of therapeutic peptides is usually a biochemical experimental method, which is time-consuming and labor-intensive. Lots of computational biology methods had been proposed to solve the problem of therapeutic peptide prediction. However, the existing methods did not consider the processing of noisy samples. We propose a kernel risk-sensitive mean p-power error-based echo state network with sparse learning (KRP-ESN-SL). An efficient iterative optimization algorithm is used to train the model. The KRP-ESN-SL has better performance than other methods.