Publication Date
1-1-2023
Journal
ESCAPE
DOI
10.1016/b978-0-443-15274-0.50418-2
PMID
37575176
PMCID
PMC10413412
PubMedCentral® Posted Date
7-18-2024
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes
Keywords
machine learning, artificial neural networks, estrogenic potential, high throughput analysis, nonlinear classification
Abstract
We develop a machine learning framework that integrates high content/high throughput image analysis and artificial neural networks (ANNs) to model the separation between chemical compounds based on their estrogenic receptor activity. Natural and man-made chemicals have the potential to disrupt the endocrine system by interfering with hormone actions in people and wildlife. Although numerous studies have revealed new knowledge on the mechanism through which these compounds interfere with various hormone receptors, it is still a very challenging task to comprehensively evaluate the endocrine disrupting potential of all existing chemicals and their mixtures by pure
Included in
Biological Phenomena, Cell Phenomena, and Immunity Commons, Biomedical Informatics Commons, Life Sciences Commons, Medical Cell Biology Commons, Medical Microbiology Commons, Medical Molecular Biology Commons, Medical Specialties Commons