Traffic and video quality with adaptive neural compression
Multimedia Systems - Special issue on multimedia networking
Task assignment and transaction clustering heuristics for distributed systems
Information Sciences: an International Journal - Special issue: load balancing in distributed systems
Random neural networks with multiple classes of signals
Neural Computation
Performance Evaluation
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology)
A self-aware approach to denial of service defence
Computer Networks: The International Journal of Computer and Telecommunications Networking
Dealing with software viruses: A biological paradigm
Information Security Tech. Report
Admission of QoS aware users in a smart network
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Random neural networks with synchronized interactions
Neural Computation
Fast Distributed Near-Optimum Assignment of Assets to Tasks
The Computer Journal
A Framework for Energy-Aware Routing in Packet Networks
The Computer Journal
Energy packet networks: adaptive energy management for the cloud
Proceedings of the 2nd International Workshop on Cloud Computing Platforms
Stochastic Gene Expression Modeling with Hill Function for Switch-Like Gene Responses
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Function approximation with spiked random networks
IEEE Transactions on Neural Networks
Learning in the multiple class random neural network
IEEE Transactions on Neural Networks
Design and implementation of a random neural network routing engine
IEEE Transactions on Neural Networks
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G-networks are a class of stochastic models that have had a broad range of applications ranging from the performance analysis of computer systems and networks to the modelling of gene regulatory networks. Gene regulatory networks consist of thousands of genes and proteins which are dynamically interacting with each other. Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviours since pathway activities could be changed by their given conditions. This review mainly focuses on a stochastic GRN modelling techniques based on G-networks which provide the analytical steady-state solution of a system for efficient GRN dynamics modelling. Three applications of the G-network model to GRNs show that this novel approach can serve to detect abnormalities from protein expression data, and that they can help to explicit the behaviour of complicated GRN models by dividing the gene regulatory processes into DNA and protein layers.