IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial neural network model for predicting HIV protease cleavage sites in protein
Advances in Engineering Software
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
An enhanced subspace method for face recognition
Pattern Recognition Letters
Using pseudo amino acid composition to predict protein subnuclear localization: Approached with PSSM
Pattern Recognition Letters
Machine learning multi-classifiers for peptide classification
Neural Computing and Applications
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Predictive vaccinology: optimisation of predictions using support vector machine classifiers
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Hi-index | 12.06 |
Research on peptide classification problems has focused mainly on the study of different encodings and the application of several classification algorithms to achieve improved prediction accuracies. The main drawback of the literature is the lack of an extensive comparison among the available encoding methods on a wide range of classification problems. This paper addresses the fundamental issue of which peptide encoding promises the best results for machine learning classifiers. Two novel encoding methods based on physicochemical properties of the amino acids are proposed and an extensive comparison with several standard encoding methods is performed on three different classification problems (HIV-protease, recognition of T-cell epitopes and prediction of peptides that bind human leukocyte antigens). The experimental results demonstrate the effectiveness of the new encodings and show that the frequently used orthonormal encoding is inferior compared to other methods.