Dataset: Machine learning-assisted identification and quantification of hydroxylated metabolites of polychlorinated biphenyls in animal samples

Chunyun Zhang; Xueshu Li; Kimberly P Keil Stietz; Sunjay Sethi; Weizhu Yang; Rachel F Marek; Xinxin Ding; Pamela J Lein; Keri C Hornbuckle; Hans-Joachim Lehmler

doi:10.25820/data.006179

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Dataset: Machine learning-assisted identification and quantification of hydroxylated metabolites of polychlorinated biphenyls in animal samples

Chunyun Zhang, Xueshu Li, Kimberly P Keil Stietz, Sunjay Sethi, Weizhu Yang, Rachel F Marek, Xinxin Ding, Pamela J Lein, Keri C Hornbuckle and Hans-Joachim Lehmler

University of Iowa

09/13/2022

DOI: 10.25820/data.006179

Files and links (11)

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Readme_OH-PCBs_MachineLearning12.41 kBDownload View

READMEDescribes files, methods, funding sources and personnel.Open Data Commons Attribution (ODC-By) V1.0, Open Access

RData

D1_model-workspace-RRT861.87 kBDownload View

Open Data Commons Attribution (ODC-By) V1.0, Open Access

RData

D2_model-workspace-MSMS35.80 MBDownload View

Open Data Commons Attribution (ODC-By) V1.0, Open Access

D3_RRT-prediction-script1.42 kBDownload View

Code/ScriptOpen Data Commons Attribution (ODC-By) V1.0, Open Access

D4_MSMS-prediction-script2.57 kBDownload View

Code/ScriptOpen Data Commons Attribution (ODC-By) V1.0, Open Access

Abstract

This dataset includes the R workspaces, R scripts, and example data for predicting the relative retention times(RRT) and MS/MS data of methoxylated metabolites of polychlorinated biphenyls (MeO-PCBs) on a gas chromatography-tandem mass spectrometry (GC-MS/MS) system. In addition, molecular descriptors of 124 MeO-PCBs, including 99 cheminformatics-based descriptors and 6 substitution pattern-based descriptors, and the measured and predicted RRT and MS/MS data of 124 MeO-PCBs are provided in separate csv files.

Machine Learning

OH-PCBs

Hydroxylated PCBs

Polychlorinated biphenyls

GC-MS/MS Method

Model Prediction

Relative Retention Time

Relative Response Factor

PCB Metabolites

Synthesis Core

Analytical Core

UC-Davis collaboration

Details

Title: Subtitle: Dataset: Machine learning-assisted identification and quantification of hydroxylated metabolites of polychlorinated biphenyls in animal samples
Creators: Chunyun Zhang - University of Iowa, Occupational and Environmental Health
Xueshu Li - University of Iowa, Occupational and Environmental Health
Kimberly P Keil Stietz - University of California, Davis
Sunjay Sethi - University of California, Davis
Weizhu Yang - University of Arizona
Rachel F Marek - University of Iowa, IIHR--Hydroscience and Engineering
Xinxin Ding
Pamela J Lein - University of California, Davis
Keri C Hornbuckle - University of Iowa, IIHR--Hydroscience and Engineering
Hans-Joachim Lehmler - University of Iowa, Occupational and Environmental Health
Resource Type: Dataset
Publisher: University of Iowa
DOI: 10.25820/data.006179
Grants: Airborne PCBs: Sources, Exposures, Toxicities, Remediation, P42ES013661, National Institutes of Health (United States, Bethesda) - NIH
Environmental Health Sciences Research Center, P30ES005605, National Institutes of Health (United States, Bethesda) - NIH
PCB-mediated dysbiosis of the gut microbiome: A missing link in PCB-mediated neurodevelopmental disorders?, R01ES031098, National Institutes of Health (United States, Bethesda) - NIH
PCB Enantiomers Implicated in Neurodevelopmental Disorders: Identification of Individual Metabolic Factors that Determine Risk and Vulnerability, R21ES027169, National Institutes of Health (United States, Bethesda) - NIH
Molecular and Cellular Basis of PCB Developmental Neurotoxicity, R01ES014901, National Institutes of Health (United States, Bethesda) - NIH
Language: English
Date collected: 12/29/2020 - 03/16/2021
Date published: 09/13/2022
Academic Unit: Civil and Environmental Engineering; IIHR--Hydroscience and Engineering; Interdisciplinary Graduate Program in Human Toxicology; Iowa Neuroscience Institute; Iowa Superfund Research Program; Occupational and Environmental Health
Record Identifier: 9984265745002771

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Dataset: Machine learning-assisted identification and quantification of hydroxylated metabolites of polychlorinated biphenyls in animal samples

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