title: Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins.
abstract: BACKGROUND
  Protein-protein interactions (PPIs) are critical for virtually every biological function. Recently, researchers suggested to use supervised learning for the task of classifying pairs of proteins as interacting or not. However, its performance is largely restricted by the availability of truly interacting proteins (labeled). Meanwhile, there exists a considerable amount of protein pairs where an association appears between two partners, but not enough experimental evidence to support it as a direct interaction (partially labeled).
  
  
  RESULTS
  We propose a semi-supervised multi-task framework for predicting PPIs from not only labeled, but also partially labeled reference sets. The basic idea is to perform multi-task learning on a supervised classification task and a semi-supervised auxiliary task. The supervised classifier trains a multi-layer perceptron network for PPI predictions from labeled examples. The semi-supervised auxiliary task shares network layers of the supervised classifier and trains with partially labeled examples. Semi-supervision could be utilized in multiple ways. We tried three approaches in this article, (i) classification (to distinguish partial positives with negatives); (ii) ranking (to rate partial positive more likely than negatives); (iii) embedding (to make data clusters get similar labels). We applied this framework to improve the identification of interacting pairs between HIV-1 and human proteins. Our method improved upon the state-of-the-art method for this task indicating the benefits of semi-supervised multi-task learning using auxiliary information.
  
  
  BACKGROUND
  http://www.cs.cmu.edu/~qyj/HIVsemi.
authors.affiliation: NEC Labs America, Princeton, NJ 08540, USA. qyj@cs.cmu.edu
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Qi
authors.forename: Oznur
authors.initials: O
authors.lastname: Tastan
authors.forename: Jaime G
authors.initials: JG
authors.lastname: Carbonell
authors.forename: Judith
authors.initials: J
authors.lastname: Klein-Seetharaman
authors.forename: Jason
authors.initials: J
authors.lastname: Weston
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D015497:HIV-1 / Q000502:physiology*; D054298:Human Immunodeficiency Virus Proteins / Q000378:metabolism*; D006801:Humans; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000378:metabolism*
chemical_list: D054298:Human Immunodeficiency Virus Proteins; D011506:Proteins
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