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238 results on '"Tox21"'

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1. High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions.

3. Tox21Enricher-Shiny: an R Shiny application for toxicity functional annotation analysis

4. Teratological and Behavioral Screening of the National Toxicology Program 91-Compound Library in Zebrafish (Danio rerio)

5. Estimating drug-induced liver injury risk by in vitro molecular initiation response and pharmacokinetic parameters for during early drug development.

7. A Binary Classification Model for Toxicity Prediction in Drug Design

8. Epidemiology meets toxicogenomics: Mining toxicologic evidence in support of an untargeted analysis of pesticides exposure and Parkinson’s disease

9. Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure.

11. Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network

12. Use of Tox21 screening data to profile PFAS bioactivities on nuclear receptors, cellular stress pathways, and cytochrome p450 enzymes.

13. Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network.

14. Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors

15. Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors.

16. Bioactivity Signatures of Drugs vs. Environmental Chemicals Revealed by Tox21 High-Throughput Screening Assays

17. Toxicity testing is evolving!

18. Review of high-content screening applications in toxicology.

19. Orthogonal assay and QSAR modelling of Tox21 PPARγ antagonist in vitro high-throughput screening assay.

20. Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure–Activity Relationship System

21. Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints

22. Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure–Activity Relationship (QSAR) Analysis

23. Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency.

24. Identifying Attributes That Influence In Vitro-to-In Vivo Concordance by Comparing In Vitro Tox21 Bioactivity Versus In Vivo DrugMatrix Transcriptomic Responses Across 130 Chemicals.

25. Multi-Behavioral Endpoint Testing of an 87-Chemical Compound Library in Freshwater Planarians.

26. A Method for in vitro Data and Structure Curation to Optimize for QSAR Modelling of Minimum Absolute Potency Levels and A Comparative Use Case

27. Prediction Is a Balancing Act: Importance of Sampling Methods to Balance Sensitivity and Specificity of Predictive Models Based on Imbalanced Chemical Data Sets

28. Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data.

29. Exploration of ToxCast/Tox21 bioassays as candidate bioanalytical tools for measuring groups of chemicals in water.

30. Infer the in vivo point of departure with ToxCast in vitro assay data using a robust learning approach.

31. New toxicogenetic insights and ranking of the selected pharmaceuticals belong to the three different classes: A toxicity estimation to confirmation approach.

32. The US Federal Tox21 Program: A Strategic and Operational Plan for Continued Leadership.

33. Investigating the Generalizability of the MultiFlow ® DNA Damage Assay and Several Companion Machine Learning Models With a Set of 103 Diverse Test Chemicals.

34. From Classical Toxicology to Tox21: Some Critical Conceptual and Technological Advances in the Molecular Understanding of the Toxic Response Beginning From the Last Quarter of the 20th Century.

36. In vitro profiling of pesticides within the Tox21 10K compound library for bioactivity and potential toxicity.

37. The RPTEC/TERT1 cell line models key renal cell responses to the environmental toxicants, benzo[a]pyrene and cadmium

38. Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network

39. Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library

40. Improving the Utility of the Tox21 Dataset by Deep Metadata Annotations and Constructing Reusable Benchmarked Chemical Reference Signatures

41. In Silico Molecular Docking and In Vivo Validation with Caenorhabditis elegans to Discover Molecular Initiating Events in Adverse Outcome Pathway Framework: Case Study on Endocrine-Disrupting Chemicals with Estrogen and Androgen Receptors

42. Elucidating environmental dimensions of neurological disorders and disease: Understanding new tools from federal chemical testing programs.

43. Incorporating ToxCast and Tox21 datasets to rank biological activity of chemicals at Superfund sites in North Carolina.

44. A method for in vitro data and structure curation to optimize for QSAR modelling of minimum absolute potency levels and a comparative use case.

46. Tox21Enricher-Shiny: an R Shiny application for toxicity functional annotation analysis.

47. Differential modulation of FXR activity by chlorophacinone and ivermectin analogs.

48. Identifying biological pathway interrupting toxins using multi-tree ensembles

49. QSAR modeling of Tox21 challenge stress response and nuclear receptor signaling toxicity assays

50. Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network

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