Navigating the Minefield of Computational Toxicology and Informatics: Looking Back and Charting a New Horizon
暂无分享,去创建一个
[1] Judy Strickland,et al. A Curated Database of Rodent Uterotrophic Bioactivity , 2015, Environmental health perspectives.
[2] David M. Reif,et al. Using Nuclear Receptor Activity to Stratify Hepatocarcinogens , 2011, PloS one.
[3] Robert G. Pearce,et al. Evaluating In Vitro-In Vivo Extrapolation of Toxicokinetics , 2018, Toxicological sciences : an official journal of the Society of Toxicology.
[4] Heather L Ciallella,et al. Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity. , 2019, Chemical research in toxicology.
[5] Steven K. Gibb. Toxicity testing in the 21st century: a vision and a strategy. , 2008, Reproductive toxicology.
[6] Ruili Huang,et al. Analysis of eight oil spill dispersants using rapid, in vitro tests for endocrine and other biological activity. , 2010, Environmental science & technology.
[7] Division on Earth. Using 21st Century Science to Improve Risk-Related Evaluations , 2017 .
[8] Richard A Becker,et al. Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes. , 2015, Regulatory toxicology and pharmacology : RTP.
[9] Ann M Richard,et al. The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency. , 2019, Toxicological sciences : an official journal of the Society of Toxicology.
[10] Nina Nikolova-Jeliazkova,et al. An Approach to Determining Applicability Domains for QSAR Group Contribution Models: An Analysis of SRC KOWWIN , 2005, Alternatives to laboratory animals : ATLA.
[11] Melvin E. Andersen,et al. Incorporating New Technologies Into Toxicity Testing and Risk Assessment: Moving From 21st Century Vision to a Data-Driven Framework , 2013, Toxicological sciences : an official journal of the Society of Toxicology.
[12] Imran Shah,et al. Navigating through the minefield of read-across frameworks: A commentary perspective , 2018 .
[13] Ryan R. Lougee,et al. Bioactivity screening of environmental chemicals using imaging-based high-throughput phenotypic profiling. , 2019, Toxicology and applied pharmacology.
[14] Nicole Kleinstreuer,et al. Systems Modeling of Developmental Vascular Toxicity. , 2019, Current opinion in toxicology.
[15] Sean Watford,et al. ToxRefDB version 2.0: Improved utility for predictive and retrospective toxicology analyses. , 2019, Reproductive toxicology.
[16] R. Woodrow Setzer,et al. tcpl: the ToxCast pipeline for high‐throughput screening data , 2016, Bioinform..
[17] Manuela Pavan,et al. The Characterisation of (Quantitative) Structure-Activity Relationships: Preliminary Guidance , 2005 .
[18] Robert G. Pearce,et al. httk: R Package for High-Throughput Toxicokinetics. , 2017, Journal of statistical software.
[19] Anton Simeonov,et al. The US Federal Tox21 Program: A strategic and operational plan for continued leadership. , 2018, ALTEX.
[20] Prachi Pradeep,et al. Estimating uncertainty in the context of new approach methodologies for potential use in chemical safety evaluation , 2019, Current Opinion in Toxicology.
[21] Thomas Hartung,et al. Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across , 2019, Environmental health perspectives.
[22] Imran Shah,et al. Considerations for Strategic Use of High-Throughput Transcriptomics Chemical Screening Data in Regulatory Decisions. , 2019, Current opinion in toxicology.
[23] L. Hall,et al. Three new consensus QSAR models for the prediction of Ames genotoxicity. , 2004, Mutagenesis.
[24] Huixiao Hong,et al. Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure. , 2015, Chemical research in toxicology.
[25] Kelly R. Moran,et al. BAYESIAN JOINT MODELING OF CHEMICAL STRUCTURE AND DOSE RESPONSE CURVES. , 2019, The annals of applied statistics.
[26] Ann M Richard,et al. Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization. , 2019, Toxicological sciences : an official journal of the Society of Toxicology.
[27] Bertrand Desprez,et al. Non-animal methods to predict skin sensitization (I): the Cosmetics Europe database* , 2018, Critical reviews in toxicology.
[28] Robert J Kavlock,et al. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. , 2012, Toxicological sciences : an official journal of the Society of Toxicology.
[29] Romualdo Benigni,et al. Data-based review of QSARs for predicting genotoxicity: the state of the art , 2019, Mutagenesis.
[30] Ruili Huang,et al. A Data Analysis Pipeline Accounting for Artifacts in Tox21 Quantitative High-Throughput Screening Assays , 2015, Journal of biomolecular screening.
[31] Kamel Mansouri,et al. Predictive Models for Acute Oral Systemic Toxicity: A Workshop to Bridge the Gap from Research to Regulation. , 2018, Computational toxicology.
[32] Scott D. Kahn,et al. Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships , 2005, Alternatives to laboratory animals : ATLA.
[33] Richard S Judson,et al. Workflow for defining reference chemicals for assessing performance of in vitro assays. , 2018, ALTEX.
[34] Andrew Worth,et al. Applying Adverse Outcome Pathways (AOPs) to support Integrated Approaches to Testing and Assessment (IATA). , 2014, Regulatory toxicology and pharmacology : RTP.
[35] Daniel P Russo,et al. Mechanism-driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data. , 2020, Toxicological sciences : an official journal of the Society of Toxicology.
[36] C. Austin,et al. Improving the Human Hazard Characterization of Chemicals: A Tox21 Update , 2013, Environmental health perspectives.
[37] John D. Walker,et al. Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances. , 2003, Environmental health perspectives.
[38] Ruili Huang,et al. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project , 2016, Environmental health perspectives.
[39] Ruili Huang,et al. Integrated Model of Chemical Perturbations of a Biological Pathway Using 18 In Vitro High-Throughput Screening Assays for the Estrogen Receptor. , 2015, Toxicological sciences : an official journal of the Society of Toxicology.
[40] Andrew P Worth,et al. Validation of Computational Methods. , 2016, Advances in experimental medicine and biology.
[41] David M. Reif,et al. Update on EPA's ToxCast program: providing high throughput decision support tools for chemical risk management. , 2012, Chemical research in toxicology.
[42] Morton Lippmann,et al. Exposure science in the 21st century: a vision and a strategy , 2013, Journal of Exposure Science and Environmental Epidemiology.
[43] R. Tennant,et al. Chemical structure, Salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP. , 1988, Mutation research.
[44] John D. Walker,et al. Use of QSARs in international decision-making frameworks to predict health effects of chemical substances. , 2003, Environmental health perspectives.
[45] Stephen W. Edwards,et al. Progress in data interoperability to support computational toxicology and chemical safety evaluation. , 2019, Toxicology and applied pharmacology.
[46] R. Kroes. Structure-Based Thresholds of Toxicological Concern (TTC): Guidance for Application to Substances Present at Low Levels in the Diet , 2004, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.
[47] Ruili Huang,et al. Editorial: Tox21 Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways As Mediated by Exposure to Environmental Toxicants and Drugs , 2017, Front. Environ. Sci..