Detecting ice artefacts in processed macromolecular diffraction data with machine learning

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Abstract
Contamination with diffraction from ice crystals can negatively affect, or even impede macromolecular structure determination and therefore, detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed, it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX webserver and CCP4-distributed software. Synopsis A program utilizing artificial learning and convolutional neural networks, named Helcaraxe, has been developed which can detect ice crystal artefacts in processed macromolecular diffraction data with unprecedented accuracy.

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