Using Machine Learning to Predict Bacterial Growth According to the Media Components | Dr Bei-Wen Ying
Original Article Reference
This SciPod is a summary of the paper: ‘Predicting the decision-making chemicals used for bacterial growth’ published in Nature Scientific Reports. DOI: https://doi.org/10.1038/s41598-019-43587-8
About this episode
Bacterial growth depends on the complex interactions of a multitude of chemical components. Microbiologists have long attempted to predict bacterial growth according to culture media components, and have employed a variety of mathematical and computational models to this end. Dr Bei-Wen Ying and her colleagues at the University of Tsukuba, Japan, successfully applied machine learning to understand the contribution of media culture components to bacterial growth. Their work makes a significant contribution to growth prediction and demonstrates that machine learning can be employed in the exploration of the complex dynamics that regulate living systems.
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Dr Samantha Dolan – Investigating Facilitators and Barriers to Electronic Immunisation Registry Implementation in Tanzania and Zambia
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