首页|Studies from University of Technology Sydney Yield New Information about Machine Learning (Applications of Machine Learning In Antibody Discovery, Process Development, Manufacturing and Formulation: Current Trends, Challenges, and Opportunities)
Studies from University of Technology Sydney Yield New Information about Machine Learning (Applications of Machine Learning In Antibody Discovery, Process Development, Manufacturing and Formulation: Current Trends, Challenges, and Opportunities)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news originating from Sydney, Australia, by NewsRx correspondents, research stated, "While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biologics, hindering the enormous potential for bioprocesses automation from their development to manufacturing. However, the adoption of ML-based models instead of conventional multivariate data analysis methods is significantly increasing due to the accumulation of large-scale production data." Financial support for this research came from Australian Research Council. Our news journalists obtained a quote from the research from the University of Technology Sydney, "This trend is primarily driven by the real-time monitoring of process variables and quality attributes of biopharmaceutical products through the implementation of advanced process analytical technologies. Given the complexity and multidimensionality of a bioproduct design, bioprocess development, and product manufacturing data, ML-based approaches are increasingly being employed to achieve accurate, flexible, and high-performing predictive models to address the problems of analytics, monitoring, and control within the biopharma field. This paper aims to provide a comprehensive review of the current applications of ML solutions in the design, monitoring, control, and optimisation of upstream, downstream, and product formulation processes of monoclonal antibodies. Finally, this paper thoroughly discusses the main challenges related to the bioprocesses themselves, process data, and the use of machine learning models in monoclonal antibody process development and manufacturing."
SydneyAustraliaAustralia and New ZealandAntibodiesBlood ProteinsCyborgsEmerging TechnologiesImmunoglobulinsImmunologyMachine LearningProteinsUniversity of Technology Sydney
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