| Literature DB >> 15585524 |
Gianluca Pollastri1, Aoife McLysaght.
Abstract
UNLABELLED: 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. AVAILABILITY: Porter is available as a public web server at http://distill.ucd.ie/porter/ CONTACT: gianluca.pollastri@ucd.ie.Mesh:
Substances:
Year: 2004 PMID: 15585524 DOI: 10.1093/bioinformatics/bti203
Source DB: PubMed Journal: Bioinformatics ISSN: 1367-4803 Impact factor: 6.937