CPPpred: prediction of cell penetrating peptides
|Title:||CPPpred: prediction of cell penetrating peptides||Authors:||Holton, Thérèse A.
Shields, Denis C.
|Permanent link:||http://hdl.handle.net/10197/10082||Date:||1-Dec-2013||Online since:||2019-04-23T12:01:31Z||Abstract:||Summary: Cell penetrating peptides (CPPs) are attracting much attention as a means of overcoming the inherently poor cellular uptake of various bioactive molecules. Here, we introduce CPPpred, a web server for the prediction of CPPs using a N-to-1 neural network. The server takes one or more peptide sequences, between 5 and 30 amino acids in length, as input and returns a prediction of how likely each peptide is to be cell penetrating. CPPpred was developed with redundancy reduced training and test sets, offering an advantage over the only other currently available CPP prediction method.||Funding Details:||Enterprise Ireland
Science Foundation Ireland
|Type of material:||Journal Article||Publisher:||Oxford University Press||Journal:||Bioinformatics||Volume:||29||Issue:||23||Start page:||3094||End page:||3096||Copyright (published version):||2013 Oxford University Press||Keywords:||Cell penetrating peptides; Peptide sequences; CPP prediction method; Neural networks; Computational biology||DOI:||10.1093/bioinformatics/btt518||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Computer Science Research Collection|
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