PDF(Pubmed)
Abstract：
Single-particle cryo-electron microscopy (cryo-EM) has recently become a mainstream technique for the structural determination of macromolecules. Typical cryo-EM workflows collect hundreds of thousands of single-particle projections from thousands of micrographs using particle-picking algorithms. However, the number of false positives selected by these algorithms is large, so that a number of different \'cleaning steps\' are necessary to decrease the false-positive ratio. Most commonly employed techniques for the pruning of false-positive particles are time-consuming and require user intervention. In order to overcome these limitations, a deep learning-based algorithm named Deep Consensus is presented in this work. Deep Consensus works by computing a smart consensus over the output of different particle-picking algorithms, resulting in a set of particles with a lower false-positive ratio than the initial set obtained by the pickers. Deep Consensus is based on a deep convolutional neural network that is trained on a semi-automatically generated data set. The performance of Deep Consensus has been assessed on two well known experimental data sets, virtually eliminating user intervention for pruning, and enhances the reproducibility and objectivity of the whole process while achieving precision and recall figures above 90%.
摘要：
单粒子低温电子显微镜（cryo-EM）最近已成为确定大分子结构的主流技术。典型的低温EM工作流程使用粒子拾取算法从数千个显微照片中收集成千上万的单粒子投影。然而,这些算法选择的误报数量很大，因此，许多不同的“清洗步骤”是必要的，以降低假阳性率。用于修剪假阳性粒子的最常用技术是耗时的并且需要用户干预。为了克服这些限制,这项工作提出了一种基于深度学习的算法，名为DeepConsensus。深度共识通过计算不同粒子拾取算法的输出的智能共识来工作，导致一组粒子的假阳性率低于采集器获得的初始组。深度共识基于深度卷积神经网络，该神经网络在半自动生成的数据集上进行训练。深度共识的表现已经在两个众所周知的实验数据集上进行了评估，几乎消除了用户对修剪的干预，并提高了整个过程的可重复性和客观性，同时实现了90%以上的准确率和召回率。
