Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
A Machine-Learning Strategy for Protein Analysis
IEEE Intelligent Systems
Using Sequence Motifs for Enhanced Neural Network Prediction of Protein Distance Constraints
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Prediction of the Number of Residue Contacts in Proteins
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Hybrid modeling, hmm/nn architectures, and protein applications
Neural Computation
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data
Data Mining and Knowledge Discovery
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
Neural Networks - Special issue on neural networks and kernel methods for structured domains
2005 Speical Issue: Graph kernels for chemical informatics
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Theoretical Computer Science
Data Mining and Knowledge Discovery
Coordination number prediction using learning classifier systems: performance and interpretability
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Automated alphabet reduction method with evolutionary algorithms for protein structure prediction
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Efficient Computation of Recursive Principal Component Analysis for Structured Input
ECML '07 Proceedings of the 18th European conference on Machine Learning
Learning to play Go using recursive neural networks
Neural Networks
Graph Neural Networks for Object Localization
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
The graph neural network model
IEEE Transactions on Neural Networks
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
Scalable Neural Networks for Board Games
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Recursive principal component analysis of graphs
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
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We describe a general methodology for the design of large-scale recursive neural network architectures (DAG-RNNs) which comprises three fundamental steps: (1) representation of a given domain using suitable directed acyclic graphs (DAGs) to connect visible and hidden node variables; (2) parameterization of the relationship between each variable and its parent variables by feedforward neural networks; and (3) application of weight-sharing within appropriate subsets of DAG connections to capture stationarity and control model complexity. Here we use these principles to derive several specific classes of DAG-RNN architectures based on lattices, trees, and other structured graphs. These architectures can process a wide range of data structures with variable sizes and dimensions. While the overall resulting models remain probabilistic, the internal deterministic dynamics allows efficient propagation of information, as well as training by gradient descent, in order to tackle large-scale problems. These methods are used here to derive state-of-the-art predictors for protein structural features such as secondary structure (1D) and both fine- and coarse-grained contact maps (2D). Extensions, relationships to graphical models, and implications for the design of neural architectures are briefly discussed. The protein prediction servers are available over the Web at: www.igb.uci.edu/tools.htm.