In:
Nature Communications, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2022-07-01)
Kurzfassung:
There is an urgent need to apply effective, data-driven approaches to reliably predict engineered nanomaterial (ENM) toxicity. Here we introduce a predictive computational framework based on the molecular and phenotypic effects of a large panel of ENMs across multiple in vitro and in vivo models. Our methodology allows for the grouping of ENMs based on multi-omics approaches combined with robust toxicity tests. Importantly, we identify mRNA-based toxicity markers and extensively replicate them in multiple independent datasets. We find that models based on combinations of omics-derived features and material intrinsic properties display significantly improved predictive accuracy as compared to physicochemical properties alone.
Materialart:
Online-Ressource
ISSN:
2041-1723
DOI:
10.1038/s41467-022-31609-5
Sprache:
Englisch
Verlag:
Springer Science and Business Media LLC
Publikationsdatum:
2022
ZDB Id:
2553671-0