LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks

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. Mos...

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Bibliographic Details
Main Authors: Seyed Amir Malekpour, Amir Reza Alizad-Rahvar, Mehdi Sadeghi
Format: Article
Language:English
Published: BMC 2020-07-01
Series:BMC Bioinformatics
Subjects:
Gene regulatory network
Probabilistic logic
Fuzzy logic
Gene expression data
Bayesian information criterion (BIC)
Bayes factor (BF)
Online Access:http://link.springer.com/article/10.1186/s12859-020-03651-x

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