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Paper   IPM / Biological Sciences / 17165
School of Biological Sciences
  Title:   wpLogicNet: logic gate and structure inference in gene regulatory networks
1.  Seyed Amir Malekpour
2.  Maryam Shahdoust
3.  Rosa Aghdam
4.  Mehdi Sadeghi
  Status:   Published
  Journal: Bioinformatics
  Year:  2023
  Supported by:  IPM
wpLogicNet proposes a framework to infer the logic gates among any number of regulators, with a low time-complexity. This distinguishes wpLogicNet from the existing logic-based models that are limited to inferring the gate between two genes or TFs. Our method applies a Bayesian mixture model to estimate the likelihood of the target gene profile and to infer the logic gate a posteriori. Furthermore, in structure-aware mode, wpLogicNet reconstructs the logic gates in TF–gene or gene–gene interaction networks with known structures. The predicted logic gates are validated on simulated datasets of TF–gene interaction networks from Escherichia coli. For the directed-edge inference, the method is validated on datasets from E.coli and DREAM project. The results show that compared to other well-known methods, wpLogicNet is more precise in reconstructing the network and logical relationships among genes.

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