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LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks.

Seyed Amir Malekpour1, Amir Reza Alizad-Rahvar2, Mehdi Sadeghi3

  • 1School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran. a.malekpour@ut.ac.ir.

BMC Bioinformatics
|July 22, 2020
PubMed
Summary

LogicNet reconstructs gene regulatory networks (GRNs) and identifies regulatory logic from continuous gene expression data without needing a predefined network structure. This novel probabilistic logic approach outperforms existing methods for accurate GRN inference.

Keywords:
Bayes factor (BF)Bayesian information criterion (BIC)Fuzzy logicGene expression dataGene regulatory networkProbabilistic logic

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Traditional gene regulatory network (GRN) inference methods often discretize continuous gene expression data, leading to information loss.
  • Existing fuzzy logic approaches require a known network structure, limiting their application.
  • Prior GRN inference techniques struggle with simultaneous structure reconstruction and logic identification.

Purpose of the Study:

  • To develop a novel logic-based approach (LogicNet) for simultaneous GRN structure reconstruction and logic identification from continuous gene expression data.
  • To introduce a probabilistic logic suitable for continuous biological data, overcoming limitations of existing fuzzy logics.
  • To infer gene-gene interactions and their logical functions without prior network knowledge.

Main Methods:

  • Developed LogicNet, a novel logic-based computational approach.
  • Introduced a new probabilistic logic designed for continuous gene expression data.
  • Applied LogicNet to simultaneously infer GRN structure and regulatory logic.

Main Results:

  • LogicNet successfully reconstructs GRNs and identifies regulatory logic without requiring a priori network structure.
  • The proposed probabilistic logic demonstrates superiority over existing fuzzy logics for biological data.
  • LogicNet's performance in GRN reconstruction surpasses that of Mutual Information-based and regression-based tools.

Conclusions:

  • LogicNet enables GRN and logic function reconstruction without prior structural knowledge.
  • The computational modeling of logical interactions significantly enhances GRN reconstruction accuracy.
  • LogicNet offers a versatile tool for both de novo GRN inference and logic detection in known interactions.