Introduction to the theory of neural computation
Introduction to the theory of neural computation
The gene expression matrix: towards the extraction of genetic network architectures
Proceedings of the second world congress on Nonlinear analysts: part 3
IPCAT '97 Proceedings of the second international workshop on Information processing in cell and tissues
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications
Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning bayesian networks from data
Learning bayesian networks from data
Qualitative simulation of genetic regulatory networks: method and application
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Logical nano-computation in enzymatic reaction networks
Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems
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The perhaps most important signaling network in living cells is constitutedby the interactions of proteins with the genome – the regulatory geneticnetwork of the cell. From a system-level point of view, the variousinteractions and control loops, which form a genetic network, represent thebasis upon which the vast complexity and flexibility of life processesemerges. Here we provide a review over some efforts towards gaining aquantitative understanding of regulatory genetic networks by means of largescale computational models. After a brief description of the biologicalprinciples of gene regulation, we summarize recent advances in massivegene-expression measurements by DNA-microarrays, which form the to date mostpowerful data basis for models of genetic networks. One class of models suchas reaction-diffusion networks and nonlinear dynamical descriptions arebiased towards using explicit molecular biological knowledge. A secondclass, centered around machine learning approaches like neural networks andBayesian networks, adopts a more data-driven approach and thereby makesmassive use of the novel gene expression measurement techniques.