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Bioinformatics Advance Access published online on September 17, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth487
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received March 3, 2004
Revised August 13, 2004
Accepted August 14, 2004

Article

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

Kuang Lin 1*, Victor A. Simossis 2, Willam R. Taylor 1, and Jaap Heringa 3

1 Division of Mathematical Biology, The National Institute for Medical Research, The Ridgeway, NW7 1AA, London, UK
2 Bioinformatics Section, Faculty of Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands
3 Bioinformatics Section, Faculty of Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands; Centre for Integrative Bioinformatics (IBIVU), Faculty of Sciences and Faculty of Earth & Life Sciences, Vrije Universiteit Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands

* To whom correspondence should be addressed.


  Abstract

Motivation: In this paper we present the secondary structure prediction method YASPIN that unlike the current state-of-the-art methods uses a single neural network to predict the secondary structure elements in a 7-state local structure scheme and then optimises the output using a Hidden Markov Model, which results in providing more information for the prediction.

Results: YASPIN was compared to the currently top-performing secondary structure prediction methods PHDpsi, PROFsec, SSPro2, JNET and PSIPRED. The overall prediction accuracy on the independent EVA5 sequence set is comparable to 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 score for strand prediction.

Availability: YASPIN is available online 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.


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