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Records 26 to
50
of 259 records from author Imran Shah
2022
Bundy, J., R. Judson, A. Williams, C. Grulke, I. Shah, AND L. Everett. A Machine Learning Approach to Predicting Molecular Initiating Events by Integrating Chemical Target Annotations with Gene Expression. Cosmetics Europe Toxicogenomics Workgroup, Virtual, NC, March 04, 2022. https://doi.org/10.23645/epacomptox.24470509
Tetko, I., G. Klambauer, D. Clevert, I. Shah, AND E. Benfenati. Artificial Intelligence Meets Toxicology. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, 35(8):1289-1290, (2022). https://doi.org/10.1021/acs.chemrestox.2c00196
Taylor, L., L. Everett, J. Harrill, I. Shah, AND R. Judson. Comparing TempO-seq and RNA-seq mRNA data sets: a case study. Genetics and Environmental Mutagenesis Society (GEMS), Durham, NC, November 01, 2022. https://doi.org/10.23645/epacomptox.21766727
Taylor, L., L. Everett, J. Harrill, I. Shah, AND R. Judson. Comparing TempO-seq and RNA-seq mRNA data sets: a case study (GEMS Meeting, Nov 2023). GEMS, Durham, NC, November 01, 2022. https://doi.org/10.23645/epacomptox.22007096
Reardon, A., L. Everett, J. Harrill, AND I. Shah. Deriving an optimal transcriptomic metric to establish protective and relevant transcriptomic points of departure for risk assessment application (ICEM, Health Canada). Presented at 13th International Conference on Environmental Mutagens, Ottawa, Ontario, CANADA, August 27 - September 01, 2022. https://doi.org/10.23645/epacomptox.25048451
Foster, M., G. Patlewicz, I. Shah, D. Haggard, R. Judson, AND K. Friedman. Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis. Computational Toxicology. Elsevier B.V., Amsterdam, Netherlands, 24:100245, (2022). https://doi.org/10.1016/j.comtox.2022.100245
Harrill, J., L. Everett, D. Haggard, J. Bundy, L. Taylor, B. Chambers, C. Willis, I. Shah, AND R. Judson. Gene Signature Concentration-Response Modeling of High-Throughput Transcriptomics (HTTr) Data for Mechanistic Prediction and Potency Estimation. Society of Toxicology 61st Annual Meeting and ToxExpo 2022, San Diego, CA, March 27 - 31, 2022. https://doi.org/10.23645/epacomptox.19400294
Everett, L., J. Harrill, R. Judson, I. Shah, J. Nyffeler, AND A. Middleton. Integration & Analysis of High-Throughput Assays in Next Generation Risk Assessment. 16th International Congress of Toxicology (IUTOX ICT 2022), MaastrichtN, September 18 - 22, 2022. https://doi.org/10.23645/epacomptox.21120187
Chambers, B., L. Taylor, N. Baker, R. Judson, AND I. Shah. Literature-mining and Transcriptomic Stress Response Annotation of a Large Chemical Database. Society of Toxicology 61st Annual Meeting and ToxExpo 2022, San Diego, CA, March 27 - 31, 2022. https://doi.org/10.23645/epacomptox.20387103
Bundy, J., R. Judson, A. Williams, C. Grulke, I. Shah, AND L. Everett. Machine Learning Prediction of Molecular Initiating Events using Chemical Target Annotations and Gene Expression. 30th Conference on Intelligent Systems for Molecular Biology (ISMB), Madison / Virtual, WI, July 10 - 14, 2022. https://doi.org/10.23645/epacomptox.20993017
Hagan, B., G. Patlewicz, AND I. Shah. Metabolic Similarity in Read-Across Prediction: A case study in using Graph Kernels and predicted metabolism information. ASCCT, Chapel Hill, NC, October 19 - 21, 2022. https://doi.org/10.23645/epacomptox.21295668
Shah, I., J. Bundy, B. Chambers, L. Everett, D. Haggard, J. Harrill, AND R. Judson. Navigating Connectivity Mapping Workflows for Predicting Molecular Targets with Gecco. Intelligent Systems for Molecular Biology, Madison, WI, July 10 - 14, 2022. https://doi.org/10.23645/epacomptox.25868806
Shah, I., J. Bundy, B. Chambers, L. Everett, D. Haggard, J. Harrill, R. Judson, J. Nyffeler, AND G. Patlewicz. Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, 35(11):1929-1949, (2022). https://doi.org/10.1021/acs.chemrestox.2c00245
Bundy, J., R. Judson, A. Williams, C. Grulke, I. Shah, AND L. Everett. Predicting Molecular Initiating Events Using Chemical Target Annotations and Gene Expression. BioData Mining. BioMed Central Ltd, London, Uk, (15):7, (2022). https://doi.org/10.1186/s13040-022-00292-z
Bundy, J., R. Judson, I. Shah, A. Williams, AND L. Everett. Predicting Molecular Initiating Events from Gene Expression using Machine Learning. MidSouth Conference on Computational Biology and Bioinformatics (MCBIOS) 2022, NA (Virtual), NC, April 25 - 27, 2022. https://doi.org/10.23645/epacomptox.19746718
Bundy, J., R. Judson, A. Williams, C. Grulke, I. Shah, AND L. Everett. Predicting Molecular Initiating Events from High Throughput Transcriptomic Screening using Machine Learning (SOT 2022). Society of Toxicology 61st Annual Meeting and ToxExpo 2022, San Diego, California, March 27 - 31, 2022. https://doi.org/10.23645/epacomptox.19528234
Taylor, L., B. Chambers, N. Baker, J. Harrill, L. Everett, I. Shah, AND R. Judson. Refining reference chemicals and signatures of activity using high throughput transcriptomics for advancing predictive toxicology. Society of Toxicology 61st Annual Meeting and ToxExpo 2022, San Diego, California, March 27 - 31, 2022. https://doi.org/10.23645/epacomptox.19539355
Patlewicz, G. AND I. Shah. Towards systematic read-across using Generalised Read-Across (GenRA). ASCCT, Chapel Hill, NC, October 19 - 21, 2022. https://doi.org/10.23645/epacomptox.21215264
Patlewicz, G. AND I. Shah. Towards systematic read-across using Generalised Read-Across (GenRA). Computational Toxicology. Elsevier B.V., Amsterdam, Netherlands, 25:100258, (2023). https://doi.org/10.1016/j.comtox.2022.100258
Vallanat, B., R. Judson, D. Haggard, L. Taylor, B. Chambers, J. Bundy, I. Shah, J. Rogers, K. Paul-Friedman, J. Harrill, AND L. Everett. Utilization of transcriptomics data in use class based grouping and classification of chemicals in toxicity testing. Society of Toxicology 61st Annual Meeting and ToxExpo 2022, San Diego, CA, March 27 - April 01, 2022.
2021
Tate, T., G. Patlewicz, AND I. Shah. Addressing Data Bias in Machine Learning for Hepatotoxicity Predictions Using Targeted Transcriptomics. American Society for Cellular and Computational Toxicology, Virtual, Virtual, October 12 - 14, 2021. https://doi.org/10.23645/epacomptox.16777645
Mortensen, H., T. Allen, J. Senn, G. Patlewicz, AND I. Shah. Assessing Machine Learning Methods in the Identification and Quantification of Environmental Chemical-Key Event Pairs Associated with Adverse Health Outcomes. Society of Toxicology 2021 Virtual Annual Meeting, RTP, NC, March 12 - 26, 2021. https://doi.org/10.23645/epacomptox.14466138
Patlewicz, G., I. Shah, T. Tate, AND W. Jenkins. Building Scientific Confidence in the Development and Application of Objective Read-across Approaches. QSAR and Read-Across in Toxicological Assessments Webinar, Durham, North Carolina, May 13, 2021. https://doi.org/10.23645/epacomptox.14465712
Jenkins, W., T. Tate, I. Shah, AND G. Patlewicz. Building a compendium of expert driven read-across (EDRA) cases to investigate the utility of New Approach Methodology (NAM) data in Generalized Read-Across (GenRA). QSAR 2021 International Workshop on QSAR in Environmental and Health Sciences, Virtual, NC, June 07 - 10, 2021. https://doi.org/10.23645/epacomptox.16455612
Haggard, D., T. Sheffield, J. Harrill, I. Shah, R. Judson, Woodrow Setzer, AND L. Everett. Determining Transcriptional Points of Departure Using a Whole Transcriptome Screening Assay. Midsouth Computational Biology & Bioinformatics Society (MCBIOS) and MAQC 2021 Joint Conference, Virtual, North Carolina, April 26 - 30, 2021. https://doi.org/10.23645/epacomptox.16899823