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Leveraging Machine Learning to Predict Toxicity
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NIEHS Superfund Research Program (SRP) grantees developed a new computational approach to predict how hazardous substances may affect health based on key changes in cells. Led by April Z. Gu, Ph.D., of the Northeastern University Puerto Rico Testsite for Exploring Contamination Threats (PROTECT) SRP Center, researchers used machine learning and advanced algorithms to link biological changes from high throughput cell studies with health outcomes observed in animal studies.
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