2005 Special Issue: Learning protein secondary structure from sequential and relational data
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Natural computing methods in bioinformatics: A survey
Information Fusion
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
Computer Methods and Programs in Biomedicine
Using classifier fusion techniques for protein secondary structure prediction
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
De Novo protein subcellular localization prediction by N-to-1 neural networks
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
PSSP with dynamic weighted kernel fusion based on SVM-PHGS
Knowledge-Based Systems
Towards designing modular recurrent neural networks in learning protein secondary structures
Expert Systems with Applications: An International Journal
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
A Comparative Study on Filtering Protein Secondary Structure Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Exploiting Intrastructure Information for Secondary Structure Prediction with Multifaceted Pipelines
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Prediction of protein secondary structure using large margin nearest neighbour classification
International Journal of Bioinformatics Research and Applications
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Cathelicidins Revisited: Molecular Evolution, Structure and Functional Implications
International Journal of Systems Biology and Biomedical Technologies
Intensity-Based Skeletonization of CryoEM Gray-Scale Images Using a True Segmentation-Free Algorithm
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 3.84 |
Summary: 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