Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms

By N. Kleinstreuer, Jian Yang, E. Berg, T. Knudsen, Ann M. Richard, Matt T. Martin, David M. Reif, Richard S. Judson, M. Polokoff, D. Dix and 2 more. 2014

191 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Small Animals

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Allan Peter Davis
2. Thomas C. Wiegers
3. Carolyn J. Mattingly
4. Daniela Sciaky
5. Richard S. Judson
6. Christopher M. Grulke
7. Christopher R'e
8. Paroma Varma

In Molecular Biology

1. Thomas C. Wiegers
2. Carolyn J. Mattingly
3. Allan Peter Davis
4. Daniela Sciaky
5. Nicola Bougen-Zhukov
6. Lit-Hsin Loo
7. Sheng Yang Michael Loh
8. Hwee Kuan Lee

In Small Animals

1. Richard S. Judson
2. Ann M. Richard

Citations in the corpus

• The Comparative Toxicogenomics Database's 10th year anniversary: update 2015 (2014, background): “In recent years, toxicology data have grown exponentially owing largely to high-throughput screening efforts both nationally (28) and internationally (29).”
• The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency. (2019, methodology): “Within EPA, the data have been used for identifying modes-of-action (MOAs) for industrial and environmental chemicals (Kleinstreuer et al. , 2014; Shah et al. , 2011), rapid testing of chemicals in emergency response situations (Judson et al. , 2010), prioritizing chemicals for analytical chemistry…”
• Large‐scale image‐based screening and profiling of cellular phenotypes (2017, background): “For other applications, such as toxicity classification (38–40), the tested extrinsic factors may have multiple, diverse, or even non-specific intracellular targets.”