Protein secondary structure prediction using distance based classifiers
International Journal of Approximate Reasoning
Prediction of Protein Beta-Sheets: Dynamic Programming versus Grammatical Approach
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Improving protein secondary structure predictions by prediction fusion
Information Fusion
Experimental Evaluation of Protein Secondary Structure Predictors
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Direct correlation analysis improves fold recognition
Computational Biology and Chemistry
Estimating the class posterior probabilities in protein secondary structure prediction
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
PSSP with dynamic weighted kernel fusion based on SVM-PHGS
Knowledge-Based Systems
A Comparative Study on Filtering Protein Secondary Structure Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Improving protein secondary structure prediction using a multi-modal BP method
Computers in Biology and Medicine
A compact hybrid feature vector for an accurate secondary structure prediction
Information Sciences: an International Journal
Cascading discriminant and generative models for protein secondary structure prediction
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
Protein structural class prediction using predicted secondary structure and hydropathy profile
Proceedings of the International C* Conference on Computer Science and Software Engineering
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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