Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Extended Kalman filter training of neural networks on a SIMD parallel machine
Journal of Parallel and Distributed Computing
Journal of Global Optimization
Linear Modeling of Genetic Networks from Experimental Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Reconstructing gene networks from large scale gene expression data
Reconstructing gene networks from large scale gene expression data
Evolving genetic regulatory networks using an artificial genome
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Analyzing time series gene expression data
Bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Dynamic tunneling technique for efficient training of multilayer perceptrons
IEEE Transactions on Neural Networks
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A gene regulatory network describes the influence of genes over others. This paper attempts to model gene regulatory network by a recurrent neural net with fuzzy membership distribution of weights. A cost function is designed to match the response of neurons in the network with the gene expression data, and a differential evolution algorithm is used to minimize the cost function. The minimization yields fuzzy membership distribution of weights, which on de-fuzzification provides the desired signed weights of the gene regulatory network. Computer simulation reveals that the proposed method outperforms existing techniques in detecting sign, and magnitude of weights of the regulatory network.