title: Prediction of subcellular localization using sequence-biased recurrent networks.
abstract: BACKGROUND
  Targeting peptides direct nascent proteins to their specific subcellular compartment. Knowledge of targeting signals enables informed drug design and reliable annotation of gene products. However, due to the low similarity of such sequences and the dynamical nature of the sorting process, the computational prediction of subcellular localization of proteins is challenging.
  
  
  RESULTS
  We contrast the use of feed forward models as employed by the popular TargetP/SignalP predictors with a sequence-biased recurrent network model. The models are evaluated in terms of performance at the residue level and at the sequence level, and demonstrate that recurrent networks improve the overall prediction performance. Compared to the original results reported for TargetP, an ensemble of the tested models increases the accuracy by 6 and 5% on non-plant and plant data, respectively.
  
  
  BACKGROUND
  The Protein Prowler incorporating the recurrent network predictor described in this paper is available online at http://pprowler.imb.uq.edu.au/
authors.affiliation: School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Australia. mikael@itee.uq.edu.au
authors.forename: Mikael
authors.initials: M
authors.lastname: Bodén
authors.forename: John
authors.initials: J
authors.lastname: Hawkins
mesh_terms: D000465:Algorithms; D005246:Feedback / Q000502:physiology; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D013347:Subcellular Fractions / Q000378:metabolism*
chemical_list: D011506:Proteins