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Application of Bayesian networks in determining nanoparticle-induced cellular outcomes using transcriptomics.

Irini Furxhi1, Finbarr Murphy1, Craig A Poland2

  • 1a Department of Accounting and Finance , Kemmy Business School University of Limerick , Limerick , Ireland.

Nanotoxicology
|May 30, 2019
PubMed
Summary

Machine learning, specifically Bayesian Networks (BNs), effectively predicts nanoparticle (NP) toxicity by linking NP properties and exposure conditions to cellular effects. This approach enhances nanotoxicity risk assessment.

Keywords:
Bayesian networksinformation gainmachine learningnanoparticlestranscriptomics

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Area of Science:

  • Nanotoxicology
  • Computational toxicology
  • Molecular toxicology

Background:

  • Understanding nanoparticle (NP) risks to human health and the environment requires ongoing research.
  • Machine learning (ML) shows promise in advancing nanotoxicology.
  • Predicting early-stage nanotoxicity and cellular effects from physicochemical properties is challenging.

Purpose of the Study:

  • To bridge the gap between NP physicochemical properties, exposure conditions, in vitro characteristics, and molecular/cellular effects using transcriptomics data.
  • To develop and implement Bayesian Networks (BNs) for modeling NP biological effects.
  • To compare different BN structuring methods (automatic vs. methodological) and data preprocessing impacts.

Main Methods:

  • Development and application of Bayesian Networks (BNs) using transcriptomics data.
  • Comparison of automatically derived BN structures versus methodologically derived ones.
  • Evaluation of BN performance with preprocessed versus unprocessed datasets.

Main Results:

  • Preprocessed data-driven BNs demonstrated superior performance compared to automatically structured BNs and those using unprocessed data.
  • Information gain analysis identified exposure dose, NP type, and cell line as key predictors of biological effects.
  • The proposed BN methodology accurately predicts NP-induced disruptions in cell cycle, proliferation, cell adhesion, DNA damage, and repair pathways (>80% success rate).

Conclusions:

  • Bayesian Networks offer a robust and promising methodology for nanotoxicity hazard and risk assessment.
  • The model effectively incorporates transcriptomics data to predict affected cellular functions based on experimental conditions.
  • This approach aids in understanding the complex relationships between NP characteristics and biological responses.