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@ARTICLE{Qi:13095,
      author       = {Qi, Y. and Tastan, O. and Carbonell, J.G. and
                      Klein-Seetharaman, J. and Weston, J.},
      title        = {{S}emi-{S}upervised {M}ulti-{T}ask {L}earning for
                      {P}redicting {I}nteractions between {HIV}-1 and {H}uman
                      {P}roteins},
      journal      = {Bioinformatics},
      volume       = {26},
      issn         = {1367-4803},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {PreJuSER-13095},
      pages        = {1645 - 1652},
      year         = {2010},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {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).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.http://www.cs.cmu.edu/~qyj/HIVsemi.},
      keywords     = {Algorithms / Artificial Intelligence / Computational
                      Biology: methods / Data Interpretation, Statistical / HIV-1:
                      physiology / Human Immunodeficiency Virus Proteins:
                      metabolism / Humans / Models, Statistical / Protein
                      Interaction Mapping: methods / Proteins: metabolism / Human
                      Immunodeficiency Virus Proteins (NLM Chemicals) / Proteins
                      (NLM Chemicals) / J (WoSType)},
      cin          = {ISB-2},
      ddc          = {004},
      cid          = {I:(DE-Juel1)ISB-2-20090406},
      pnm          = {BioSoft: Makromolekulare Systeme und biologische
                      Informationsverarbeitung},
      pid          = {G:(DE-Juel1)FUEK505},
      shelfmark    = {Biochemical Research Methods / Biotechnology $\&$ Applied
                      Microbiology / Computer Science, Interdisciplinary
                      Applications / Mathematical $\&$ Computational Biology /
                      Statistics $\&$ Probability},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:20823334},
      pmc          = {pmc:PMC2935441},
      UT           = {WOS:000281714100035},
      doi          = {10.1093/bioinformatics/btq394},
      url          = {https://juser.fz-juelich.de/record/13095},
}