15 In Silico Prediction of the Point of Departure (POD) …
313
30. Moffat I, Chepelev NL, Labib S, Bourdon-Lacombe J, Kuo B et al (2015) Comparison of
toxicogenomics and traditional approaches to inform mode of action and points of departure
in human health risk assessment of benzo [a] pyrene in drinking water. Crit Rev Toxicol
45(1):1–43
31. Labib S, Williams A, Yauk CL, Nikota JK, Wallin H et al (2015) Nano-risk science: application
of toxicogenomics in an adverse outcome pathway framework for risk assessment of multiwalled carbon nanotubes. Part Fibre Toxicol 13(1):15
32. Dean JL, Zhao QJ, Lambert JC, Hawkins BS, Thomas RS, Wesselkamper SC (2017) Application of gene set enrichment analysis for identification of chemically induced, biologically
relevant transcriptomic networks and potential utilization in human health risk assessment.
Toxicol Sci 157(1):85–99
33. Filer DL, Kothiya P, Setzer RW, Judson RS, Martin MT (2016) tcpl: the ToxCast pipeline for
high-throughput screening data. Bioinformatics 33(4):618–620
34. Wang D (2018) Infer the in vivo point of departure with ToxCast in vitro assay data using a
robust learning approach. Arch Toxicol 92(9):2913–2922
35. Shah I, Setzer RW, Jack J, Houck KA, Judson RS, Knudsen TB et al (2016) Using ToxCast™
data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure.
Environ Health Perspect 124(7):910–9
36. Sipes NS, Wambaugh JF, Pearce R, Auerbach SS, Wetmore BA et al (2017) An intuitive
approach for predicting potential human health risk with the Tox21 10K library. Environ Sci
Technol 51(18):10786–10796
37. Pearce RG, Setzer RW, Strope CL, Sipes NS, Wambaugh JF (2017) Httk: R package for highthroughput toxicokinetics. J Stat Softw 79(4):1–26
38. Mav D, Shah RR, Howard BE, Auerbach SS, Bushel PR et al (2018) A hybrid gene selection
approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics.
PLoS ONE 13(2):e0191105
Dong Wang received his Ph.D. in Genetics from Iowa State University in 2003 and received
his Ph.D. in Statistics in 2006 also from Iowa State University. He was a faculty member in the
Department of Statistics, University of Nebraska-Lincoln from 2006 to 2014 with the rank of
assistant professor and later associate professor. He worked for three years as the leader of Statistics and Mathematics Group at Dow AgroSciences between 2014 and 2016. He joined National
Center for Toxicological Research in December 2016 as a senior statistician in the Division of
Bioinformatics and Biostatistics. He has research interest in risk assessment, statistical genomics,
statistical machine learning, high-dimensional modeling, and Bayesian methods.
313
30. Moffat I, Chepelev NL, Labib S, Bourdon-Lacombe J, Kuo B et al (2015) Comparison of
toxicogenomics and traditional approaches to inform mode of action and points of departure
in human health risk assessment of benzo [a] pyrene in drinking water. Crit Rev Toxicol
45(1):1–43
31. Labib S, Williams A, Yauk CL, Nikota JK, Wallin H et al (2015) Nano-risk science: application
of toxicogenomics in an adverse outcome pathway framework for risk assessment of multiwalled carbon nanotubes. Part Fibre Toxicol 13(1):15
32. Dean JL, Zhao QJ, Lambert JC, Hawkins BS, Thomas RS, Wesselkamper SC (2017) Application of gene set enrichment analysis for identification of chemically induced, biologically
relevant transcriptomic networks and potential utilization in human health risk assessment.
Toxicol Sci 157(1):85–99
33. Filer DL, Kothiya P, Setzer RW, Judson RS, Martin MT (2016) tcpl: the ToxCast pipeline for
high-throughput screening data. Bioinformatics 33(4):618–620
34. Wang D (2018) Infer the in vivo point of departure with ToxCast in vitro assay data using a
robust learning approach. Arch Toxicol 92(9):2913–2922
35. Shah I, Setzer RW, Jack J, Houck KA, Judson RS, Knudsen TB et al (2016) Using ToxCast™
data to reconstruct dynamic cell state trajectories and estimate toxicological points of departure.
Environ Health Perspect 124(7):910–9
36. Sipes NS, Wambaugh JF, Pearce R, Auerbach SS, Wetmore BA et al (2017) An intuitive
approach for predicting potential human health risk with the Tox21 10K library. Environ Sci
Technol 51(18):10786–10796
37. Pearce RG, Setzer RW, Strope CL, Sipes NS, Wambaugh JF (2017) Httk: R package for highthroughput toxicokinetics. J Stat Softw 79(4):1–26
38. Mav D, Shah RR, Howard BE, Auerbach SS, Bushel PR et al (2018) A hybrid gene selection
approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics.
PLoS ONE 13(2):e0191105
Dong Wang received his Ph.D. in Genetics from Iowa State University in 2003 and received
his Ph.D. in Statistics in 2006 also from Iowa State University. He was a faculty member in the
Department of Statistics, University of Nebraska-Lincoln from 2006 to 2014 with the rank of
assistant professor and later associate professor. He worked for three years as the leader of Statistics and Mathematics Group at Dow AgroSciences between 2014 and 2016. He joined National
Center for Toxicological Research in December 2016 as a senior statistician in the Division of
Bioinformatics and Biostatistics. He has research interest in risk assessment, statistical genomics,
statistical machine learning, high-dimensional modeling, and Bayesian methods.