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Machine learning to predict developmental neurotoxicity with high-throughput data from 2D bio-engineered tissues
There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as in vivo animal studies, and assays of animal and human primary cell cultures, suffer from challenges related to time, cost, and applicab...
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| Publicat a: | Proc Int Conf Mach Learn Appl |
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| Autors principals: | , , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
2020
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7075697/ https://ncbi.nlm.nih.gov/pubmed/32181450 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/icmla.2019.00055 |
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