Positive-unlabeled learning for the prediction of conformational B-cell epitopes.

ABSTRACT:

Background

The incomplete ground truth of training data of B-cell epitopes is a demanding issue in computational epitope prediction. The challenge is that only a small fraction of the surface residues of an antigen are confirmed as antigenic residues (positive training data); the remaining residues are unlabeled. As some of these uncertain residues can possibly be grouped to form novel but currently unknown epitopes, it is misguided to unanimously classify all the unlabeled residues as negative training data following the traditional supervised learning scheme.

Results

We propose a positive-unlabeled learning algorithm to address this problem. The key idea is to distinguish between epitope-likely residues and reliable negative residues in unlabeled data. The method has two s

SUBMITTER: Ren J 

PROVIDER: S-EPMC4682424 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature