Predicting Dose-Dependent Carcinogenicity of Chemical Mixtures Using a Novel Hybrid Neural Network Framework and Mathematical Approach

This study addresses the challenge of assessing the carcinogenic potential of hazardous chemical mixtures, such as per- and polyfluorinated substances (PFASs), which are known to contribute significantly to cancer development. Here, we propose a novel framework called HNN(MixCancer) that utilizes a...

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Detalles Bibliográficos
Autores principales: Limbu, Sarita, Dakshanamurthy, Sivanesan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383376/
https://www.ncbi.nlm.nih.gov/pubmed/37505571
http://dx.doi.org/10.3390/toxics11070605

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383376/
https://www.ncbi.nlm.nih.gov/pubmed/37505571
http://dx.doi.org/10.3390/toxics11070605

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