Advancing Computational Toxicology by Interpretable Machine Learning. [PDF]
Jia X, Wang T, Zhu H.
europepmc +3 more sources
Navigating the Minefield of Computational Toxicology and Informatics: Looking Back and Charting a New Horizon. [PDF]
Patlewicz G.
europepmc +3 more sources
From QSAR to QSIIR: searching for enhanced computational toxicology models. [PDF]
Zhu H.
europepmc +3 more sources
Introduction to Special Issue: Computational Toxicology [PDF]
Nicole C. Kleinstreuer+2 more
openaire +4 more sources
Decoding per- and polyfluoroalkyl substances (PFAS) in hepatocellular carcinoma: a multi-omics and computational toxicology approach. [PDF]
Hong Y+5 more
europepmc +2 more sources
Progress in data interoperability to support computational toxicology and chemical safety evaluation. [PDF]
Watford S+4 more
europepmc +3 more sources
Editorial: Leveraging artificial intelligence and open science for toxicological risk assessment [PDF]
Marc Teunis+3 more
doaj +2 more sources
Current computational technologies hold promise for prioritizing the testing of the thousands of chemicals in commerce. Here, a case study is presented demonstrating comparative risk-prioritization approaches based on the ratio of surrogate hazard and ...
Chantel I. Nicolas+10 more
doaj +1 more source
The ToxCast in vitro screening program has provided concentration-response bioactivity data across more than a thousand assay endpoints for thousands of chemicals found in our environment and commerce.
Jill A. Franzosa+12 more
doaj +1 more source
Casting a wide net: use of diverse model organisms to advance toxicology [PDF]
© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hahn, M. E., & Sadler, K. C. Casting a wide net: use of diverse model organisms to advance toxicology.
Hahn, Mark E., Sadler, Kirsten C.
core +1 more source