Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data.

ABSTRACT: Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computation

SUBMITTER: Aghayev Z 

PROVIDER: S-EPMC10448728 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature