Preparing samples for proteomics has traditionally required a time-consuming and error-prone multi-step workflow that, according to literature, causes 75 percent of the overall variability in proteomics data
New deep learning software for analyzing the choreography of protein movement removes the need for expert human intervention and opens up the field to more labs worldwide