title: 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 the most valuable samples and add them into the original training set, to help to retrain the classifiers. Numerical experiments with for four popular multi-label classifiers on three benchmark datasets show that the proposed method can effectively select the valuable samples to supplement the original training set and significantly improve the performances of predicting classifiers.
authors.affiliation: School of Control Science and Engineering, Dalian University of Technology, Dalian, China.
authors.forename: Junzhe
authors.initials: J
authors.lastname: Cao
authors.forename: Wenqi
authors.initials: W
authors.lastname: Liu
authors.forename: Jianjun
authors.initials: J
authors.lastname: He
authors.forename: Hong
authors.initials: H
authors.lastname: Gu
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D042541:Intracellular Space / Q000378:metabolism*; D058977:Molecular Sequence Annotation; D021381:Protein Transport; D011506:Proteins / Q000378:metabolism*
chemical_list: D011506:Proteins