296
R. Huang
24. Huang R, Southall N, Xia M, Cho MH, Jadhav A, Nguyen DT, Inglese J, Tice RR, Austin
CP (2009) Weighted feature significance: a simple, interpretable model of compound toxicity
based on the statistical enrichment of structural features. Toxicol Sci 112(2):385–393
25. Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental
evaluation tool in clinical medicine. Clin Chem 39(4):561–577
26. Allen JA, Roth BL (2011) Strategies to discover unexpected targets for drugs active at G
protein-coupled receptors. Annu Rev Pharmacol Toxicol 51:117–144
27. Lynch T, Price A (2007) The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects. Am Fam Physician 76(3):391–396
28. Martin MT, Knudsen TB, Reif DM, Houck KA, Judson RS, Kavlock RJ, Dix DJ (2011)
Predictive model of rat reproductive toxicity from ToxCast high throughput screening. Biol
Reprod 85(2):327–339
29. Sipes NS, Martin MT, Reif DM, Kleinstreuer NC, Judson RS, Singh AV, Chandler KJ, Dix DJ,
Kavlock RJ, Knudsen TB (2011) Predictive models of prenatal developmental toxicity from
ToxCast high-throughput screening data. Toxicol Sci 124(1):109–127
30. Sipes NS, Wambaugh JF, Pearce R, Auerbach SS, Wetmore BA, Hsieh JH, Shapiro AJ, Svoboda
D, DeVito MJ, Ferguson SS (2017) An intuitive approach for predicting potential human health
risk with the Tox21 10K library. Environ Sci Technol 51(18):10786–10796
31. Judson RS, Houck KA, Kavlock RJ, Knudsen TB, Martin MT, Mortensen HM, Reif DM,
Rotroff DM, Shah I, Richard AM, Dix DJ (2010) In vitro screening of environmental chemicals
for targeted testing prioritization: the ToxCast project. Environ Health Perspect 118(4):485–492
32. Sun H, Veith H, Xia M, Austin CP, Tice RR, Huang R (2012) Prediction of cytochrome P450
profiles of environmental chemicals with QSAR models built from drug-like molecules. Mol
Inform 31(11–12):783–792
33. Judson R, Richard A, Dix DJ, Houck K, Martin M, Kavlock R, Dellarco V, Henry T, Holderman
T, Sayre P, Tan S, Carpenter T, Smith E (2009) The toxicity data landscape for environmental
chemicals. Environ Health Perspect 117(5):685–695
34. Muster W, Breidenbach A, Fischer H, Kirchner S, Muller L, Pahler A (2008) Computational
toxicology in drug development. Drug Discov Today 13(7–8):303–310
35. Vedani A, Smiesko M (2009) In silico toxicology in drug discovery—concepts based on threedimensional models. Altern Lab Anim 37(5):477–496
36. Huang R, Xia M, Nguyen D-T, Zhao T, Sakamuru S, Zhao J, Shahane SA, Rossoshek A,
Simeonov A (2016) Tox21 challenge to build predictive models of nuclear receptor and stress
response pathways as mediated by exposure to environmental chemicals and drugs. Front
Environ Sci 3(85):1–9
37. Huang R, Xia M (2016) Research topic: Tox21 challenge to build predictive models of nuclear
receptor and stress response pathways as mediated by exposure to environmental toxicants and
drugs. Front Environ Sci 2954
38. Abdelaziz A, Spahn-Langguth H, Schramm K-W, Tetko IV (2016) Consensus modeling for
HTS assays using in silico descriptors calculates the best balanced accuracy in Tox21 challenge.
Front Environ Sci 4(2):1–12
39. Barta G (2016) Identifying biological pathway interrupting toxins using multi-tree ensembles.
Front Environ Sci 4:52
40. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
41. Mayr A, Klambauer G, Unterthiner T, Hochreiter S (2016) DeepTox: toxicity prediction using
deep learning. Front Environ Sci 3(80):1–15
42. USEPA (2017) ToxCast data. Available from http://www2.epa.gov/chemical-research/toxicityforecaster-toxcasttm-data
43. FDA (2004) Innovation or stagnation: challenge and opportunity on the critical path to new
medical products
44. Martic-Kehl MI, Schibli R, Schubiger PA (2012) Can animal data predict human outcome? Problems and pitfalls of translational animal research. Eur J Nucl Med Mol Imaging
39(9):1492–1496
R. Huang
24. Huang R, Southall N, Xia M, Cho MH, Jadhav A, Nguyen DT, Inglese J, Tice RR, Austin
CP (2009) Weighted feature significance: a simple, interpretable model of compound toxicity
based on the statistical enrichment of structural features. Toxicol Sci 112(2):385–393
25. Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental
evaluation tool in clinical medicine. Clin Chem 39(4):561–577
26. Allen JA, Roth BL (2011) Strategies to discover unexpected targets for drugs active at G
protein-coupled receptors. Annu Rev Pharmacol Toxicol 51:117–144
27. Lynch T, Price A (2007) The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects. Am Fam Physician 76(3):391–396
28. Martin MT, Knudsen TB, Reif DM, Houck KA, Judson RS, Kavlock RJ, Dix DJ (2011)
Predictive model of rat reproductive toxicity from ToxCast high throughput screening. Biol
Reprod 85(2):327–339
29. Sipes NS, Martin MT, Reif DM, Kleinstreuer NC, Judson RS, Singh AV, Chandler KJ, Dix DJ,
Kavlock RJ, Knudsen TB (2011) Predictive models of prenatal developmental toxicity from
ToxCast high-throughput screening data. Toxicol Sci 124(1):109–127
30. Sipes NS, Wambaugh JF, Pearce R, Auerbach SS, Wetmore BA, Hsieh JH, Shapiro AJ, Svoboda
D, DeVito MJ, Ferguson SS (2017) An intuitive approach for predicting potential human health
risk with the Tox21 10K library. Environ Sci Technol 51(18):10786–10796
31. Judson RS, Houck KA, Kavlock RJ, Knudsen TB, Martin MT, Mortensen HM, Reif DM,
Rotroff DM, Shah I, Richard AM, Dix DJ (2010) In vitro screening of environmental chemicals
for targeted testing prioritization: the ToxCast project. Environ Health Perspect 118(4):485–492
32. Sun H, Veith H, Xia M, Austin CP, Tice RR, Huang R (2012) Prediction of cytochrome P450
profiles of environmental chemicals with QSAR models built from drug-like molecules. Mol
Inform 31(11–12):783–792
33. Judson R, Richard A, Dix DJ, Houck K, Martin M, Kavlock R, Dellarco V, Henry T, Holderman
T, Sayre P, Tan S, Carpenter T, Smith E (2009) The toxicity data landscape for environmental
chemicals. Environ Health Perspect 117(5):685–695
34. Muster W, Breidenbach A, Fischer H, Kirchner S, Muller L, Pahler A (2008) Computational
toxicology in drug development. Drug Discov Today 13(7–8):303–310
35. Vedani A, Smiesko M (2009) In silico toxicology in drug discovery—concepts based on threedimensional models. Altern Lab Anim 37(5):477–496
36. Huang R, Xia M, Nguyen D-T, Zhao T, Sakamuru S, Zhao J, Shahane SA, Rossoshek A,
Simeonov A (2016) Tox21 challenge to build predictive models of nuclear receptor and stress
response pathways as mediated by exposure to environmental chemicals and drugs. Front
Environ Sci 3(85):1–9
37. Huang R, Xia M (2016) Research topic: Tox21 challenge to build predictive models of nuclear
receptor and stress response pathways as mediated by exposure to environmental toxicants and
drugs. Front Environ Sci 2954
38. Abdelaziz A, Spahn-Langguth H, Schramm K-W, Tetko IV (2016) Consensus modeling for
HTS assays using in silico descriptors calculates the best balanced accuracy in Tox21 challenge.
Front Environ Sci 4(2):1–12
39. Barta G (2016) Identifying biological pathway interrupting toxins using multi-tree ensembles.
Front Environ Sci 4:52
40. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
41. Mayr A, Klambauer G, Unterthiner T, Hochreiter S (2016) DeepTox: toxicity prediction using
deep learning. Front Environ Sci 3(80):1–15
42. USEPA (2017) ToxCast data. Available from http://www2.epa.gov/chemical-research/toxicityforecaster-toxcasttm-data
43. FDA (2004) Innovation or stagnation: challenge and opportunity on the critical path to new
medical products
44. Martic-Kehl MI, Schibli R, Schubiger PA (2012) Can animal data predict human outcome? Problems and pitfalls of translational animal research. Eur J Nucl Med Mol Imaging
39(9):1492–1496