首页|Data on Artificial Intelligence Published by a Researcher at Mario Negri Institute for Pharmacological Research (Artificial Intelligence and Machine Learning Methods to Evaluate Cardiotoxicity following the Adverse Outcome Pathway Frameworks)
Data on Artificial Intelligence Published by a Researcher at Mario Negri Institute for Pharmacological Research (Artificial Intelligence and Machine Learning Methods to Evaluate Cardiotoxicity following the Adverse Outcome Pathway Frameworks)
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Investigators publish new report on artificial intelligence. According to news reporting out of Milan, Italy, by NewsRx editors, research stated, “Cardiovascular disease is a leading global cause of mortality. The potential cardiotoxic effects of chemicals from different classes, such as environmental contaminants, pesticides, and drugs can significantly contribute to effects on health.” Funders for this research include European Union's Horizon 2020 Research And Innovation Program. Our news correspondents obtained a quote from the research from Mario Negri Institute for Pharmacological Research: “The same chemical can induce cardiotoxicity in different ways, following various Adverse Outcome Pathways (AOPs). In addition, the potential synergistic effects between chemicals further complicate the issue. In silico methods have become essential for tackling the problem from different perspectives, reducing the need for traditional in vivo testing, and saving valuable resources in terms of time and money. Artificial intelligence (AI) and machine learning (ML) are among today's advanced approaches for evaluating chemical hazards. They can serve, for instance, as a first-tier component of Integrated Approaches to Testing and Assessment (IATA). This study employed ML and AI to assess interactions between chemicals and specific biological targets within the AOP networks for cardiotoxicity, starting with molecular initiating events (MIEs) and progressing through key events (KEs). We explored methods to encode chemical information in a suitable way for ML and AI. We started with commonly used approaches in Quantitative Structure-Activity Relationship (QSAR) methods, such as molecular descriptors and different types of fingerprint.”
Mario Negri Institute for Pharmacological ResearchMilanItalyEuropeArtificial IntelligenceChemicalsCyborgsEmerging TechnologiesMachine Learning
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