Learning versus optimal intervention in random Boolean networks
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DOI: 10.1007/s41109-019-0243-z
Abstract: Random Boolean Networks (RBNs) are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine learning, specifically through… read more here.
Keywords: learning versus; random boolean; versus optimal; boolean networks ... See more keywords
Attractor-Specific and Common Expression Values in Random Boolean Network Models (with a Preliminary Look at Single-Cell Data)
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DOI: 10.3390/e24030311
Abstract: Random Boolean Networks (RBNs for short) are strongly simplified models of gene regulatory networks (GRNs), which have also been widely studied as abstract models of complex systems and have been used to simulate different phenomena.… read more here.
Keywords: network; random boolean; single cell; specific common ... See more keywords
Temporal, Structural, and Functional Heterogeneities Extend Criticality and Antifragility in Random Boolean Networks
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DOI: 10.3390/e25020254
Abstract: Most models of complex systems have been homogeneous, i.e., all elements have the same properties (spatial, temporal, structural, functional). However, most natural systems are heterogeneous: few elements are more relevant, larger, stronger, or faster than… read more here.
Keywords: random boolean; boolean networks; temporal structural; antifragility ... See more keywords