# Prediction of skin sensitization potency using machine learning approaches

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/prediction-of-skin-sensitization-potency-using-machine-learning-approaches/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Qingda Zang,Michaël Paris,David M. Lehmann,Shannon Bell,Nicole Kleinstreuer,David Allen,Joanna Matheson,Abigail Jacobs,Warren Casey,Judy Strickland |
| Citations | 67 |
| DOI | 10.1002/jat.3424 |
| Fields | Medicine,Veterinary |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2571007314 |
| PMID | 28074598 |
| Type | article |
| Year | 2017 |

## Paper authors

- [David M. Lehmann](https://scholariq.org/researchers/david-m-lehmann/)

## Paper journal

- [Journal of Applied Toxicology](https://scholariq.org/journals/journal-of-applied-toxicology/)

## Paper primary topic

- [Contact Dermatitis and Allergies](https://scholariq.org/topics/contact-dermatitis-and-allergies/)

## Paper topics

- [Contact Dermatitis and Allergies](https://scholariq.org/topics/contact-dermatitis-and-allergies/)
- [Animal testing and alternatives](https://scholariq.org/topics/animal-testing-and-alternatives/)
- [Allergic Rhinitis and Sensitization](https://scholariq.org/topics/allergic-rhinitis-and-sensitization/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.