Unknown

Dataset Information

0

Biomarkers of nanomaterials hazard from multi-layer data.


ABSTRACT: 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.

SUBMITTER: Fortino V 

PROVIDER: S-EPMC9249793 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

altmetric image

Publications


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 i  ...[more]

Similar Datasets

| S-EPMC5877510 | biostudies-literature
| S-EPMC6154922 | biostudies-literature
| S-EPMC6723683 | biostudies-literature
| S-EPMC9370172 | biostudies-literature
| S-EPMC11674189 | biostudies-literature
| S-EPMC11875790 | biostudies-literature
| S-EPMC11415972 | biostudies-literature
| S-EPMC5794071 | biostudies-literature
| S-EPMC10366886 | biostudies-literature
| S-EPMC9921680 | biostudies-literature