A simple and fast secondary structure prediction method using hidden neural networks

Bioinformatics. 2005 Jan 15;21(2):152-9. doi: 10.1093/bioinformatics/bth487. Epub 2004 Sep 17.


Motivation: In this paper, we present a secondary structure prediction method YASPIN that unlike the current state-of-the-art methods utilizes a single neural network for predicting the secondary structure elements in a 7-state local structure scheme and then optimizes the output using a hidden Markov model, which results in providing more information for the prediction.

Results: YASPIN was compared with the current top-performing secondary structure prediction methods, such as PHDpsi, PROFsec, SSPro2, JNET and PSIPRED. The overall prediction accuracy on the independent EVA5 sequence set is comparable with that of the top performers, according to the Q3, SOV and Matthew's correlations accuracy measures. YASPIN shows the highest accuracy in terms of Q3 and SOV scores for strand prediction.

Availability: YASPIN is available on-line at the Centre for Integrative Bioinformatics website (http://ibivu.cs.vu.nl/programs/yaspinwww/) at the Vrije University in Amsterdam and will soon be mirrored on the Mathematical Biology website (http://www.mathbio.nimr.mrc.ac.uk) at the NIMR in London.

Contact: kxlin@nimr.mrc.ac.uk

Publication types

  • Comparative Study
  • Evaluation Study
  • Validation Study

MeSH terms

  • Algorithms*
  • Amino Acid Sequence
  • Artificial Intelligence
  • Markov Chains
  • Models, Chemical
  • Models, Molecular*
  • Models, Statistical
  • Molecular Sequence Data
  • Neural Networks, Computer*
  • Protein Structure, Secondary*
  • Proteins / chemistry*
  • Sequence Alignment / methods*
  • Sequence Analysis, Protein / methods*
  • Software


  • Proteins