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This paper investigates the inference of Boolean networks with perturbation (BNp) from simulated data and observed data. We interpret the discretised gene expression levels as attractor states of the underlying network and use the sequence of attractor states to determine the model. We consider the case where a complete sequence of attractors is known and the case where the known attractor states...
We propose a modeling approach based on Probabilistic Boolean Networks for the inference of genetic regulatory networks from gene expression time-course data in different biological conditions i.e. making use of the information contained in sets of genes and the interaction between genes rather than single-gene analyses. This model is a collection of traditional Probabilistic Boolean Networks. We...
This paper presents a new method of fitting probabilistic Boolean networks (PBNs) to time-course state data. The critical issue to be addressed is to identify the contributions of the PBN's constituent Boolean networks in a sequence of temporal data. The sequence must be partitioned into sections, each corresponding to a single model with fixed parameters. We propose an approach to subsequence identification...
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