BLOMAP: AN ENCODING OF AMINO ACIDS WHICH IMPROVES SIGNAL PEPTIDE CLEAVAGE SITE PREDICTION

摘要

Research on cleavage site prediction for signal peptides has focused mainly on the application of different classification algorithms to achieve improved prediction accuracies. This paper addresses the fundamental issue of amino acid encoding to present ammo acid sequences in the most beneficial way for machine learning algorithms. A comparison of several standard encoding methods shows, that for cleavage site prediction the frequently used orthonormal encoding is inferior compared to other methods.The best results are achieved with a new encoding method named BLOMAP - based on the BLOSUM62 substitution matrix - using a Naive Bayes classifier.