Code for Predicting MIEs from Gene Expression and Chemical Target Labels with Machine Learning (MIEML)
Modeling data and analysis scripts generated during the current study are available in the github repository: https://github.com/USEPA/CompTox-MIEML. RefChemDB is available for download as supplemental material from its original publication (PMID: 30570668). LINCS gene expression data are publicly available and accessible through the gene expression omnibus (GSE92742 and GSE70138) at https://www.ncbi.nlm.nih.gov/geo/ .
This dataset is associated with the following publication:
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, issue}: 7, (2022).
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"020:00"
]
|
| contactPoint |
{
"fn": "Logan Everett",
"hasEmail": "mailto:everett.logan@epa.gov"
}
|
| description | Modeling data and analysis scripts generated during the current study are available in the github repository: https://github.com/USEPA/CompTox-MIEML. RefChemDB is available for download as supplemental material from its original publication (PMID: 30570668). LINCS gene expression data are publicly available and accessible through the gene expression omnibus (GSE92742 and GSE70138) at https://www.ncbi.nlm.nih.gov/geo/ . This dataset is associated with the following publication: 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, issue}: 7, (2022). |
| distribution |
[
{
"title": "https://github.com/USEPA/CompTox-MIEML",
"accessURL": "https://github.com/USEPA/CompTox-MIEML"
}
]
|
| identifier | https://doi.org/10.23719/1524750 |
| keyword |
[
"Library of integrated cellular signatures",
"binary classification",
"chemical safety screening",
"high throughput transcriptomics",
"machine learning",
"molecular initiating events"
]
|
| license | https://pasteur.epa.gov/license/sciencehub-license-non-epa-generated.html |
| modified | 2022-03-02 |
| programCode |
[
"020:000"
]
|
| publisher |
{
"name": "U.S. EPA Office of Research and Development (ORD)",
"subOrganizationOf": {
"name": "U.S. Environmental Protection Agency",
"subOrganizationOf": {
"name": "U.S. Government"
}
}
}
|
| references |
[
"https://doi.org/10.1186/s13040-022-00292-z"
]
|
| rights |
null
|
| title | Code for Predicting MIEs from Gene Expression and Chemical Target Labels with Machine Learning (MIEML) |
