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1,226
Metabolic stability and epigenesis in randomly connected nets
- Journal of Theoretical Biology
, 1969
"... “The world is either the effect of cause or chance. If the latter, it is a world for all that, that is to say, it is a regular and beautiful structure.” Marcus Aurelius Proto-organisms probably were randomly aggregated nets of chemical reactions. The hypothesis that contemporary organisms are also r ..."
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Cited by 657 (5 self)
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; and under the stimulus of noise are capable of differentiating directly from any mode of behavior to at most a few other modes of behavior. Cellular differentation is modeled as a Markov chain among the modes of behavior of a genetic net. The possibility of a general theory of metabolic behavior
Reveal, A General Reverse Engineering Algorithm For Inference Of Genetic Network Architectures
, 1998
"... Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/o ..."
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Cited by 344 (5 self)
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/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able
BIOINFORMATICS Graphical interface to the genetic network database GeNet
"... Summary: We designed a Java applet which enables the visualization of genetic networks and can be used as a Web publishing tool by molecular biologists studying the mechanisms of gene interactions. ..."
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Summary: We designed a Java applet which enables the visualization of genetic networks and can be used as a Web publishing tool by molecular biologists studying the mechanisms of gene interactions.
A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks
- Genetic Programming 1996: Proceedings of the First Annual Conference
, 1996
"... This paper compares the efficiency of two encoding schemes for Artificial Neural Networks optimized by evolutionary algorithms. Direct Encoding encodes the weights for an a priori fixed neural network architecture. Cellular Encoding encodes both weights and the architecture of the neural netw ..."
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Cited by 170 (0 self)
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learning to compute the velocity. A new fitness function is introduced that forces the neural net...
Nets, Nets, Genetic Genetic Algorithms Algorithms and and Simulation
"... Abstract: Abstract: This paper focuses on handling disruptions in Flexible Manufacturing Systems (FMS). There are several issues which make this problem especially difficult to translate to real life systems. First the lack of appropriate tools which both capture the essence of such systems and prov ..."
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formalism; in the second stage a near optimal schedule is generated by a Genetic Algorithm (GA) which uses the Petri Net model for its chromosomal representation. This GA is also used as a reactive scheduler in the third stage where the system is simulated. In particular we focus on the impact
PowerMarker: an integrated analysis environment for genetic marker analysis
- Bioinformatics 2005
"... * * To whom correspondence should be addressed Summary: PowerMarker delivers a data-driven, integrated analysis environment (IAE) for genetic data. The IAE integrates data management, analysis, and visualization in a user-friendly graphical user interface. It accelerates the analysis lifecycle and e ..."
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Cited by 116 (0 self)
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and enables users to maintain data integrity throughout the process. An ever-growing list of more than 50 different statistical analyses for genetic markers has been implemented in PowerMarker. Availability: www.powermarker.net
Genetic regulatory net...
"... We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we do this without specifying the function performed by the network. Specifically, we study the maximum mutual info ..."
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information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species
machine learning, and genetic neural nets
- Advances in Applied Mathematics
, 1989
"... We consider neural nets whose connections are defined by growth rules taking the form of recursion relations. These are called genetic neural nets. Learning in these nets is achieved by simulated annealing optimization of the net over the space of recursion relation parameters. The method is tested ..."
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Cited by 15 (1 self)
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We consider neural nets whose connections are defined by growth rules taking the form of recursion relations. These are called genetic neural nets. Learning in these nets is achieved by simulated annealing optimization of the net over the space of recursion relation parameters. The method is tested
Pruning Neural Nets by Genetic Algorithm
- Proceedings of the International Conference on Artificial Neural Networks
, 1992
"... A genetic algorithm (GA) is used to prune the connections of backpropagation trained neural nets, used for identification of faces and of sonar signals. A fully-connected net is first trained on the target problem. The GA then prunes connections prior to retraining of the remainder. This process pro ..."
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Cited by 10 (0 self)
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A genetic algorithm (GA) is used to prune the connections of backpropagation trained neural nets, used for identification of faces and of sonar signals. A fully-connected net is first trained on the target problem. The GA then prunes connections prior to retraining of the remainder. This process
Genetic Programming for Image Analysis
- Genetic Programming 1996: Proceedings of the First Annual Conference
, 1996
"... This paper describes an approach to using GP for image analysis based on the idea that image enhancement, feature detection and image segmentation can be re-framed as filtering problems. GP can discover efficient optimal filters which solve such problems but in order to make the search feasib ..."
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Cited by 77 (8 self)
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in medical images and is compared with artificial neural nets. 1 Introduction Genetic Programming (GP) has been applied successfully to a large number of difficult problems like automatic design, pattern recognition, robotic control, synthesis on neural architectures, symbolic regression, music
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