首页|Annotating CryoET Volumes: A Machine Learning Challenge
Annotating CryoET Volumes: A Machine Learning Challenge
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – According to news reporting based on a preprint abstract, our journalists obtained thefollowing quote sourced from bi orxiv.org:“Cryo-electron tomography (cryoET) has emerged as a powerful structural biology tool for understandingprotein complexes in their native cellular environments. Presently, 3D volumes of cellular environmentscan be acquired in the thousands in a few days where each volume provides a rich and complex cellularlandscape. Despite numerous innovations, localizing and identifying the vast majority of pr otein species inthese volumes remains prohibitively difficult. Machine learning based methods provide an opportunity toautomate the process of labeling and an notating cryoET volumes. Due to current bottlenecks in the annotationprocess, a nd a lack of large standardized datasets, training datasets for machine learning algorithmshave been scarce. Here, we present a defined phantom sample, along w ith ground truth annotations,that will be the basis of a machine learning chall enge to bring cryoET and ML experts together and spurcreativity to address this annotation problem.
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