Predicting Nanomaterials toxicity pathways based on genome-wide transcriptomics studies using Bayesian networks

Irini Furxhi, Finbarr Murphy, Barry Sheehan, Martin Mullins, Paride Mantecca

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The potential toxicity of Nanomaterials (NMs) is widely documented but risk assessment continues to pose a challenge. In this study, data derived from toxicogenomic studies are used to build a Bayesian Network (BN) model. This approach integrates transcriptomics data to successfully predict a number of biological effects. The model uses experimental conditions such as dose, duration and cell type along with NM physicochemical properties, and is developed to predict the effects of NM exposure on in vitro biological systems. The model version proposed in this study is shown to successfully predict a number of biological processes with a success rate >80% for most outcomes. The model validation shows a robust and promising methodology for incorporating transcriptomics studies in a hazard and, extendedly, risk assessment modelling framework.

Original languageEnglish
Title of host publication18th International Conference on Nanotechnology, NANO 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781538653364
DOIs
Publication statusPublished - 2 Jul 2018
Externally publishedYes
Event18th International Conference on Nanotechnology, NANO 2018 - Cork, Ireland
Duration: 23 Jul 201826 Jul 2018

Publication series

NameProceedings of the IEEE Conference on Nanotechnology
Volume2018-July
ISSN (Print)1944-9399
ISSN (Electronic)1944-9380

Conference

Conference18th International Conference on Nanotechnology, NANO 2018
Country/TerritoryIreland
CityCork
Period23/07/1826/07/18

Fingerprint

Dive into the research topics of 'Predicting Nanomaterials toxicity pathways based on genome-wide transcriptomics studies using Bayesian networks'. Together they form a unique fingerprint.

Cite this