Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
A framework for path analysis in gene regulatory networks
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
SplitNet: a dynamic hierarchical network model
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
IEEE Communications Magazine
MCMC Based Bayesian Inference for Modeling Gene Networks
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
A Markov-Blanket-Based Model for Gene Regulatory Network Inference
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Reconstructing GRN from microarray dataset is a very challenging problem as these datasets typically have large number of genes and less number of samples. Moreover, the reconstruction task becomes further complicated as there are no suitable synthetic datasets available for validation and evaluation of GRN reconstruction techniques. Synthetic datasets allow validating new techniques and approaches since the underlying mechanisms of the GRNs, generated from these datasets, are completely known. In this paper, we present an approach for synthetically generating gene networks using causal relationships. The synthetic networks can have varying topologies such as small world, random, scale free, or hierarchical topologies based on the well-defined GRN properties. These artificial but realistic GRN networks provide a simulation environment similar to a real-life laboratory microarray experiment. These networks also provide a mechanism for studying the robustness of reconstruction methods to individual and combination of parametric changes such as topology, noise (background and experimental noise) and time delays. Studies involving complicated interactions such as feedback loops, oscillations, bi-stability, dynamic behavior, vertex in-degree changes and number of samples can also be carried out by the proposed synthetic GRN networks.