Stegle and Theis Group

Cellular Genetics Programme

We aim to leverage machine learning in the context of single cell genomics to provide a true model-based understanding of the manifold upcoming organ atlases. In particular we will develop and adapt methods from deep learning to find robust latent spaces and then combine this with interpretation models to find gene regulatory patterns.

For more about the two Associate Faculty leading this group, please click on their names below.

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    [excerpt] => We aim to leverage machine learning in the context of single cell genomics to provide a true model-based understanding of the manifold upcoming organ atlases. In particular we will develop and adapt methods from deep learning to find robust latent spaces and then combine this with interpretation models to find gene regulatory patterns.
    [introduction] => We aim to leverage machine learning in the context of single cell genomics to provide a true model-based understanding of the manifold upcoming organ atlases. In particular we will develop and adapt methods from deep learning to find robust latent spaces and then combine this with interpretation models to find gene regulatory patterns.
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