4 Works

Parametric Design File and STLs For Apical Imaging Chamber (AIC)

Jens Eriksson, Jorik van Rijn & Mikael Sellin
Interactions between individual pathogenic microbes and host tissues involve fast and dynamic processes that ultimately impact the outcome of infection. Using live cell microscopy, these dynamics can be visualized to study e.g. microbe motility, binding and invasion of host cells, and intra-host-cell survival. Such methodology typically employs confocal imaging of fluorescent tags in tumor-derived cell line infections on glass. This allows high-definition imaging, but poorly reflects the host tissue's physiological architecture and may result in...

Parametric Design File and STLs For Apical Imaging Chamber (AIC)

Jens Eriksson, Jorik van Rijn & Mikael Sellin
Interactions between individual pathogenic microbes and host tissues involve fast and dynamic processes that ultimately impact the outcome of infection. Using live cell microscopy, these dynamics can be visualized to study e.g. microbe motility, binding and invasion of host cells, and intra-host-cell survival. Such methodology typically employs confocal imaging of fluorescent tags in tumor-derived cell line infections on glass. This allows high-definition imaging, but poorly reflects the host tissue's physiological architecture and may result in...

Random forest models for gene expression experiments in Transformational Machine Learning

Ivan Olier, Oghenejokpeme Orhobor, Tirtharaj Dash, Andy Davis, Larisa N. Soldatova, Joaquin Vanschoren & Ross King
Almost all machine learning (ML) is based on representing examples using intrinsic features. When there are multiple related ML problems (tasks), it is possible to transform these features into extrinsic features by first training ML models on other tasks and letting them each make predictions for each example of the new task, yielding a novel representation.
We call this transformational ML (TML). TML is very closely related to, and synergistic with, transfer learning, multi-task learning,...

Random forest models for gene expression experiments in Transformational Machine Learning

Ivan Olier, Oghenejokpeme Orhobor, Tirtharaj Dash, Andy Davis, Larisa N. Soldatova, Joaquin Vanschoren & Ross King
Almost all machine learning (ML) is based on representing examples using intrinsic features. When there are multiple related ML problems (tasks), it is possible to transform these features into extrinsic features by first training ML models on other tasks and letting them each make predictions for each example of the new task, yielding a novel representation.
We call this transformational ML (TML). TML is very closely related to, and synergistic with, transfer learning, multi-task learning,...

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