Much of the work carried out by DTT is in support of the National Toxicology Program (NTP), an interagency partnership of the Food and Drug Administration, National Institute for Occupational Safety and Health, and NIEHS.
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Scott S. Auerbach, Ph.D.
Leader, Toxicoinformatics
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Tel 984-287-3108
Fax 919-541-3647
[email protected]
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530 Davis Drive (Keystone Bldg)
Research Summary
The Toxicoinformatics Group leads the development of data analytic approaches related to Tox21 data and provides informatics support for a variety of projects within the Biomolecular Screening Branch and across the National Toxicology Program.
Specific projects include:
- Development of toxicogenomic models that predict apical toxicological outcomes
- Evaluation of methods for scaling toxicogenomic technologies
- In vitro / In vivo extrapolation
- Analytics pipeline development for Tox21 high throughput screening data
- Development of network-based inference approaches for biological read-across using Tox21 data
- Development of software for genomic benchmark dose analysis
Scientists & Staff
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Scott S. Auerbach, Ph.D.
Leader, Toxicoinformatics
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Tel 984-287-3108
Fax 919-541-3647
[email protected]
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Frank G. Chao, Ph.D.
Computational Biologist
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Tel 984-287-4066
Fax 919-541-3647
[email protected]
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Jui-Hua Hsieh, Ph.D.
Staff Scientist
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Tel 984-287-3142
[email protected]
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Fred Parham, Ph.D.
Mathematical Statistician
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Tel 984-287-3169
[email protected]
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Trey O. Saddler
Contractor - Data Scientist
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Tel 984-287-3180
[email protected]
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Zicong Wang
Contractor - Software Developer
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Tel 984-287-4494
[email protected]
Recent Publications
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Corton J, Auerbach S, Koyama N, Mezencev R, Yauk C, Suzuki T. Review and meta-analysis of gene expression biomarkers predictive of chemical-induced genotoxicity in vivo.
Environmental and molecular mutagenesis.
2025 Jan 21 [Epub ahead of print].
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AbstractCorton J, Auerbach S, Koyama N, Mezencev R, Yauk C, Suzuki T. Review and meta-analysis of gene expression biomarkers predictive of chemical-induced genotoxicity in vivo. Environmental and molecular mutagenesis. 2025 Jan 21
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Pannala V, Hari A, Abdulhameed M, Balik-Meisner M, Mav D, Phadke D, Scholl E, Shah R, Auerbach S, Wallqvist A. Quantifying liver-toxic responses from dose-dependent chemical exposures using a rat genome-scale metabolic model.
Toxicological sciences : an official journal of the Society of Toxicology.
2025 Jan 17 [Epub ahead of print].
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AbstractPannala V, Hari A, Abdulhameed M, Balik-Meisner M, Mav D, Phadke D, Scholl E, Shah R, Auerbach S, Wallqvist A. Quantifying liver-toxic responses from dose-dependent chemical exposures using a rat genome-scale metabolic model. Toxicological sciences : an official journal of the Society of Toxicology. 2025 Jan 17
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Froetschl R, Corton J, Li H, Aubrecht J, Auerbach S, Caiment F, Doktorova T, Fujita Y, Jennen D, Koyama N, Meier M, Mezencev R, Recio L, Suzuki T, Yauk C. Consensus findings of an International Workshops on Genotoxicity Testing workshop on using transcriptomic biomarkers to predict genotoxicity.
Environmental and molecular mutagenesis.
2025 Jan 05 [Epub ahead of print].
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AbstractFroetschl R, Corton J, Li H, Aubrecht J, Auerbach S, Caiment F, Doktorova T, Fujita Y, Jennen D, Koyama N, Meier M, Mezencev R, Recio L, Suzuki T, Yauk C. Consensus findings of an International Workshops on Genotoxicity Testing workshop on using transcriptomic biomarkers to predict genotoxicity. Environmental and molecular mutagenesis. 2025 Jan 05
]
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Zilber D, Messier K, House J, Parham F, Auerbach S, Wheeler M. Bayesian gene set benchmark dose estimation for "omic" responses.
Bioinformatics (Oxford, England).
2024 Dec 26;41(1):.
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AbstractZilber D, Messier K, House J, Parham F, Auerbach S, Wheeler M. Bayesian gene set benchmark dose estimation for "omic" responses. Bioinformatics (Oxford, England). 2024 Dec 26
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Pannala V, Balik-Meisner M, Mav D, Phadke D, Scholl E, Shah R, Auerbach S, Wallqvist A. Correction: Pannala et al. High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model. Int. J. Mol. Sci. 2023, 24, 17425.
International journal of molecular sciences.
2024 Jun 28;25(13):.
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AbstractPannala V, Balik-Meisner M, Mav D, Phadke D, Scholl E, Shah R, Auerbach S, Wallqvist A. Correction: Pannala et al. High-Throughput Transcriptomics Differentiates Toxic versus Non-Toxic Chemical Exposures Using a Rat Liver Model. Int. J. Mol. Sci. 2023, 24, 17425. International journal of molecular sciences. 2024 Jun 28
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More Recent Publications from PubMed
Software
- Tox21 Toolbox
The Tox21 toolbox contains useful tools for accessing and visualizing the Tox21 quantitative high throughput screening (qHTS) 10K library data, as well as integrating with other publicly available data.
