Positive-Unlabeled Learning for Pupylation Sites Prediction.

ABSTRACT: Pupylation plays a key role in regulating various protein functions as a crucial posttranslational modification of prokaryotes. In order to understand the molecular mechanism of pupylation, it is important to identify pupylation substrates and sites accurately. Several computational methods have been developed to identify pupylation sites because the traditional experimental methods are time-consuming and labor-sensitive. With the existing computational methods, the experimentally annotated pupylation sites are used as the positive training set and the remaining nonannotated lysine residues as the negative training set to build classifiers to predict new pupylation sites from the unknown proteins. However, the remaining nonannotated lysine residues may contain pupylation sites which have n

SUBMITTER: Jiang M 

PROVIDER: S-EPMC4992543 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature