NIEluter: Predicting peptides eluted from HLA class I molecules.The immune system has evolved to make a diverse repertoire of peptides processed from self and foreign proteomes, which are displayed in antigen-binding grooves of major histocompatibility complex (MHC) proteins at cell surface for surveillance by T cells.  In this paper, we describe NIEluter, an ensemble predictor based on support vector machine (SVM).  NIEluter can predict NPPs of length 8-11 from six HLA alleles (A0201, B0702, B3501, B4403, B5301, and B5701) at present.  Evaluated with five-fold cross-validation and independent datasets if available, NIEluter shows good performance.  NIEluter has been implemented as a free web service, which can be accessed at http://immunet.cn/nie/cgi-bin/nieluter.pl.
NIEluter
In this paper, we describe NIEluter, an ensemble predictor based on support vector machine (SVM).  It consists of a combination of five SVM models trained with position-specific amino acid composition, position-specific dipeptide composition, Hidden Markov Model, binary encoding, and BLOSUM62 feature. 
SVM
It consists of a combination of five SVM models trained with position-specific amino acid composition, position-specific dipeptide composition, Hidden Markov Model, binary encoding, and BLOSUM62 feature. 
Hidden Markov Model
It consists of a combination of five SVM models trained with position-specific amino acid composition, position-specific dipeptide composition, Hidden Markov Model, binary encoding, and BLOSUM62 feature. 
BLOSUM62
It outperforms MHC-NP in 7 out of 24 types of situation and precedes NetMHC3.2 in most cases, indicating that it is a helpful complement to available tools. 
MHC-NP
It outperforms MHC-NP in 7 out of 24 types of situation and precedes NetMHC3.2 in most cases, indicating that it is a helpful complement to available tools. 
NetMHC3.2