The cascade-correlation learning architecture
Advances in neural information processing systems 2
Combining GP operators with SA search to evolve fuzzy rule based classifiers
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Neuro-Control and Its Applications
Neuro-Control and Its Applications
Flexible neural trees ensemble for stock index modeling
Neurocomputing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Small-time scale network traffic prediction based on flexible neural tree
Applied Soft Computing
Evolving computer programs without subtree crossover
IEEE Transactions on Evolutionary Computation
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Use of a quasi-Newton method in a feedforward neural network construction algorithm
IEEE Transactions on Neural Networks
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The advances on DNA microarray technologies have enabled researchers to gain hundreds to thousands of gene expression levels. Much effect has been devoted over the past decade to analyze the gene expression data. In this study, flexible neural tree (FNT) model is used for gene regulatory network reconstruction and time-series prediction from gene expression profiling. We use voting strategy and Akaike information criterion (AIC) as two methods to identifying minimal regulatory elements of a target gene. A simulated dataset and three real biological datasets are used to test the validity of the FNT model. Results reveal that the FNT model can improve the prediction accuracy of microarray time-series data effectively and reconstruct gene regulatory network accurately.