KELM-CPPpred is a web-server, which facilitates the prediction of cell penetrating peptides (CPPs) based on Kernel-Extreme Learning Machine. In KELM-CPPpred user can select one or more than one classification models based on various feature vectors and their hybrid implimentation to be cell penetrating.
User can provide a sequence of minimum length 5 and maximum upto length 30. Please note that multiple query sequences are allowed. Please see the example for input sequences. User can select upto 6 prediction model. For detailed information about input features or model selection, please check our paper entitled "KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides". We recommand to use Hybrid-PseAAC-KELM model, based on the results as described in the paper.
The tool will predict the input peptide sequence (or multiple sequences) as cell penetrating or non-cell penetrating.
Poonam Pandey, Vinal Patel, Nithin V. George and Sairam S. Mallajosyula "KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides". (Manuscript just accepted in J Proteome Res. 2018 Aug 13)
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