Mining Proteins with Non-Experimental Annotations Based on an Active Sample Selection Strategy for Predicting Protein Subcellular Localization.

ABSTRACT: Subcellular localization of a protein is important to understand proteins' functions and interactions. There are many techniques based on computational methods to predict protein subcellular locations, but it has been shown that many prediction tasks have a training data shortage problem. This paper introduces a new method to mine proteins with non-experimental annotations, which are labeled by non-experimental evidences of protein databases to overcome the training data shortage problem. A novel active sample selection strategy is designed, taking advantage of active learning technology, to actively find useful samples from the entire data pool of candidate proteins with non-experimental annotations. This approach can adequately estimate the "value" of each sample, automatically select th

SUBMITTER: Cao J 

PROVIDER: S-EPMC3694045 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature