A machine learning approach to protein structure prediction
A machine learning approach to protein structure prediction
Bidirectional recurrent neural networks
IEEE Transactions on Signal Processing
Learning long-term dependencies in NARX recurrent neural networks
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Towards designing modular recurrent neural networks in learning protein secondary structures
Expert Systems with Applications: An International Journal
A Comparative Study on Filtering Protein Secondary Structure Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
Customized prediction of respiratory motion with clustering from multiple patient interaction
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against another two BRNN architectures, namely the original BRNN architecture used for speech recognition as well as Pollastri's BRNN that was proposed for PSS prediction. Our cascaded BRNN achieves an overall three state accuracy Q3 of 74.38\%, and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6%.