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Paper   IPM / Biological Sciences / 16288
School of Biological Sciences
  Title:   LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks
  Author(s): 
1.  Seyed Amir Malekpour
2.  Amir Reza Alizad-Rahvar
3.  Mehdi Sadeghi
  Status:   Published
  Journal: BMC Bioinformatics
  No.:  318
  Vol.:  21
  Year:  2020
  Pages:   1-21
  Supported by:  IPM
  Abstract:
Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the data, resulting in missing information. Most of studies apply fuzzy logics to infer the logical gene-gene interactions from continuous data. However, all these approaches require an a priori known network structure.<br> https://doi.org/10.1186/s12859-020-03651-x


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