A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training with noise is equivalent to Tikhonov regularization
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Bayesian Methods for Elucidating Genetic Regulatory Networks
IEEE Intelligent Systems
Evolutionary modeling and inference of gene network
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
Learning state space trajectories in recurrent neural networks
Neural Computation
A Bayesian approach to learning causal networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Information Sciences: an International Journal
Evolving gene regulatory networks: a sensitivity-based approach
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Using gene expression programming to infer gene regulatory networks from time-series data
Computational Biology and Chemistry
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Constructing genetic regulatory networks is one of the most important issues in system biology research. Yet, building regulatory models manually is a tedious task, especially when the number of genes involved increases with the complexity of regulation. To automate the procedure of network construction, in this work we establish a clustering-based approach to infer recurrent neural networks as regulatory systems. Our approach also deals with the scalability problem by developing a clustering method with several data analysis techniques. To verify the presented approach, experiments have been conducted and the results show that it can be used to infer gene regulatory networks successfully.