title: Porter: a new, accurate server for protein secondary structure prediction.
abstract: Porter is a new system for protein secondary structure prediction in three classes. Porter relies on bidirectional recurrent neural networks with shortcut connections, accurate coding of input profiles obtained from multiple sequence alignments, second stage filtering by recurrent neural networks, incorporation of long range information and large-scale ensembles of predictors. Porter's accuracy, tested by rigorous 5-fold cross-validation on a large set of proteins, exceeds 79%, significantly above a copy of the state-of-the-art SSpro server, better than any system published to date.
  
  
  BACKGROUND
  Porter is available as a public web server at http://distill.ucd.ie/porter/
  
  
  BACKGROUND
  gianluca.pollastri@ucd.ie.
authors.affiliation: Computer Science Department, University College Dublin, Belfield, Dublin 4, Ireland. gianluca.pollastri@ucd.ie
authors.forename: Gianluca
authors.initials: G
authors.lastname: Pollastri
authors.forename: Aoife
authors.initials: A
authors.lastname: McLysaght
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D020407:Internet; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins