title: Semi-supervised protein subcellular localization.
abstract: BACKGROUND
  Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data.
  
  
  RESULTS
  In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions.
  
  
  CONCLUSIONS
  Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.
authors.affiliation: Program of Bioengineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. fleurxq@ust.hk
authors.forename: Qian
authors.initials: Q
authors.lastname: Xu
authors.forename: Derek Hao
authors.initials: DH
authors.lastname: Hu
authors.forename: Hong
authors.initials: H
authors.lastname: Xue
authors.forename: Weichuan
authors.initials: W
authors.lastname: Yu
authors.forename: Qiang
authors.initials: Q
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008954:Models, Biological; D021381:Protein Transport; D011506:Proteins / Q000032:analysis* / Q000737:chemistry
chemical_list: D011506:Proteins