首页|Study Findings on Machine Learning Published by Researchers at Department of Gen omics (Leveraging multi-omics and machine learning approaches in malting barley research: From farm cultivation to the final products)
Study Findings on Machine Learning Published by Researchers at Department of Gen omics (Leveraging multi-omics and machine learning approaches in malting barley research: From farm cultivation to the final products)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting out of the Department of Ge nomics by NewsRx editors, research stated, “This study focuses on the potential of multi-omics and machine learning approaches in improving our understanding of the malting processes and cultivation systems in barley.” Financial supporters for this research include Abrii. Our news correspondents obtained a quote from the research from Department of Ge nomics: “The omics approach has been used to explore biomarkers associated with desired sensory characteristics in malting barley, enabling potential applicatio ns in specific treatments to modify diastatic power, enzyme activity, color, and aroma compounds. Moreover, the integration of machine learning and multi-omics in malting barley researches has significantly enhanced our knowledge in physiol ogy, cultivation, and processing for more efficient and sustainable production s ystems in malting barley industry. The integration of cutting-edge machine visio n and high-throughput phenotyping technologies has additionally the potential to revolutionize the assessment of physical and biochemical traits in malting barl ey. In addition, the harnessing of integrative approach to predict consumer acce ptability, and assess physicochemical and colorimetric properties of malt extrac ts has been discussed. Current survey showed that the ML-driven predictive maint enance is revolutionizing the barley malting industry by not only enhancing equi pment performance but also minimizing operational costs and reducing unplanned d owntime.”
Department of GenomicsCyborgsEmergin g TechnologiesMachine Learning
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