LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks.

ABSTRACT:

Background

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.

Results

Here, by introducing a new probabilistic logic for continuous data, we propose a novel logic-based approach (called the LogicNet) for the simultaneous reconstruction of the GRN structure and identification of the logics among the regulatory genes, from the continuous gene expression data. In contrast to the prev

SUBMITTER: Malekpour SA 

PROVIDER: S-EPMC7372900 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature