Application of Cascade Correlation Networks for Structures toChemistry
Applied Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Learning precise timing with lstm recurrent networks
The Journal of Machine Learning Research
The Journal of Machine Learning Research
The Applicability of Recurrent Neural Networks for Biological Sequence Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On Learning Vector-Valued Functions
Neural Computation
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Learning long-term dependencies with gradient descent is difficult
IEEE Transactions on Neural Networks
Natural computing methods in bioinformatics: A survey
Information Fusion
Neural network for graphs: a contextual constructive approach
IEEE Transactions on Neural Networks
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Computer Methods and Programs in Biomedicine
Towards designing modular recurrent neural networks in learning protein secondary structures
Expert Systems with Applications: An International Journal
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We propose a method for sequential supervised learning that exploits explicit knowledge of short- and long-range dependencies. The architecture consists of a recursive and bi-directional neural network that takes as input a sequence along with an associated interaction graph. The interaction graph models (partial) knowledge about long-range dependency relations. We tested the method on the prediction of protein secondary structure, a task in which relations due to beta-strand pairings and other spatial proximities are known to have a significant effect on the prediction accuracy. In this particular task, interactions can be derived from knowledge of protein contact maps at the residue level. Our results show that prediction accuracy can be significantly boosted by the integration of interaction graphs.