TY - GEN
T1 - Predicting Nanomaterials toxicity pathways based on genome-wide transcriptomics studies using Bayesian networks
AU - Furxhi, Irini
AU - Murphy, Finbarr
AU - Sheehan, Barry
AU - Mullins, Martin
AU - Mantecca, Paride
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85062282880&partnerID=8YFLogxK
U2 - 10.1109/NANO.2018.8626300
DO - 10.1109/NANO.2018.8626300
M3 - Conference contribution
AN - SCOPUS:85062282880
T3 - Proceedings of the IEEE Conference on Nanotechnology
BT - 18th International Conference on Nanotechnology, NANO 2018
PB - IEEE Computer Society
T2 - 18th International Conference on Nanotechnology, NANO 2018
Y2 - 23 July 2018 through 26 July 2018
ER -