It’s capable of handling high throughput experiments, which is why we originally wrote it, but some of the same things that make it robust and reliable for high throughput experiments also make it very handy for small-scale experiments. I wrote the first version of it 16 years ago, so it’s now quite a mature project and used by thousands of biologists around the world. Can you tell us more about the quantification tools that you have developed in your lab? Letting deep learning algorithms loose on images is an unbiased way of taking the raw image data and converting it into a feature space that can go beyond what we can see by eye. You can look at so many different combinations of features of cells that it can go beyond what we are able to articulate and engineer an algorithm to specifically measure. Typically, deep learning extracts features that humans may not have considered looking for.
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