Emma Walker, Ph.D
Bacteria; microbiome; cell biology.
Our algorithm was able to successfully predict the presence and the location of the nuclei in more than 8,000 cells, with almost half of those predictions resulting in a deviation of less than 1 μm from their exact position. This demonstrated, with astounding significance, the hypothesis of a deterministic relation between the arrangements of the actin filaments and the position of the nucleus, one of the most basic relations in cell biology, which the researchers argue that this has also resulted in an epistemological outcome.
Our techniques have transformed the way we think about adapting our scientific research methods to allow machine learning to not just be used as a tool to analyse data, but to also interpret reality. For the inherently complex systems in biology, this will accelerate the next technological revolution – the ‘biologisation’ of technology. This will enable the complexities and intricacies of biological systems to be truly unravelled and dominated using machine learning.
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