摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-根据基于预印摘要的新闻报道，我们的记者获得了以下报价来源于BI orxiv.org：“低温电子断层摄影术(cryoET)已经成为一种强大的结构生物学工具，用于理解天然细胞环境中的蛋白质复合物。目前，细胞环境的三维体积可以在几天内获得数以千计的细胞，每个细胞体积提供丰富而复杂的细胞风景。尽管进行了许多创新，但对中国绝大多数的植物进行了定位和鉴定这些卷仍然非常困难。基于机器学习的方法为自动标记和标记冷冻体积的过程。由于注释中的当前瓶颈过程，以及缺乏大型标准化数据集，机器学习算法的训练数据集很少。在这里，我们展示了一个定义的幻影样本，连同地面真相注释，这将是一项机器学习挑战的基础，将cryoET和ML专家聚集在一起，并刺激创造性地解决这个注释问题。
Abstract
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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