首页|Report Summarizes Machine Learning Study Findings from University of Connecticut (Machine Learning Methods for Endocrine Disrupting Potential Identification Based On Single-cell Data)
Report Summarizes Machine Learning Study Findings from University of Connecticut (Machine Learning Methods for Endocrine Disrupting Potential Identification Based On Single-cell Data)
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NETL
NSTL
Elsevier
Current study results on Machine Learning have been published. According to news reporting out of Storrs, Connecticut, by NewsRx editors, research stated, “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.” Funders for this research include National Institutes of Health (NIH) - USA, University of Connecticut, University of Connecticut Storrs High Performance Computing facility, Integrated Microscopy Core at Baylor College of Medicine, National Institutes of Health (NIH) - USA, Cancer Prevention & Research Institute of Texas. Our news journalists obtained a quote from the research from the University of Connecticut, “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 (ER alpha), 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 computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ER alpha pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ER alpha agonists and antagonists.”
StorrsConnecticutUnited StatesNorth and Central AmericaChemicalsCyborgsEmerging TechnologiesEndocrine ResearchHealth and MedicineMachine LearningSupport Vector MachinesVector MachinesUniversity of Connecticut
NETL
NSTL
Elsevier
