In the past decade, deep conditional generative models have revolutionized
the generation of realistic images, extending their application from
entertainment to scientific domains. Single-particle cryo-electron microscopy
(cryo-EM) is crucial in resolving near-atomic resolution 3D structures of
proteins, such as the SARS-COV-2 spike protein. To achieve high-resolution
reconstruction, AI models for particle picking and pose estimation have been
adopted. However, their performance is still limited as they lack high-quality
annotated datasets. To address this, we introduce physics-informed generative
cryo-electron microscopy (GenEM), which for the first time integrates
physical-based cryo-EM simulation with a generative unpaired noise translation
to generate physically correct synthetic cryo-EM datasets with realistic
noises. Initially, GenEM simulates the cryo-EM imaging process based on a
virtual specimen. To generate realistic noises, we leverage an unpaired noise
translation via contrastive learning with a novel mask-guided sampling scheme.
Extensive experiments show that GenEM is capable of generating realistic
cryo-EM images. The generated dataset can further enhance particle picking and
pose estimation models, eventually improving the reconstruction resolution. We
will release our code and annotated synthetic datasets.

提出了一种新的方法 ——GenEM，该方法是将物理准确的冷冻电镜模拟和生成式非成对噪声转换相结合，生成具有真实噪声的合成冷冻电镜数据集，用于改善高分辨率蛋白质结构的重建分辨率。

GenEM: 物理信息生成式低温电子显微镜

GenEM: Physics-Informed Generative Cryo-Electron Microscopy

Deep conditional generative models are developed to simultaneously learn the
temporal dependencies of multiple sequences. The model is designed by
introducing a three-way weight tensor to capture the multiplicative
interactions between side information and sequences. The proposed model builds
on the Temporal Sigmoid Belief Network (TSBN), a sequential stack of Sigmoid
Belief Networks (SBNs). The transition matrices are further factored to reduce
the number of parameters and improve generalization. When side information is
not available, a general framework for semi-supervised learning based on the
proposed model is constituted, allowing robust sequence classification.
Experimental results show that the proposed approach achieves state-of-the-art
predictive and classification performance on sequential data, and has the
capacity to synthesize sequences, with controlled style transitioning and
blending.

本文提出了一种基于深度条件生成模型（DCGM）的通用框架，能够同时学习多个序列之间的时序依赖关系，并通过引入三维权重张量来捕捉附加信息和序列之间的乘法交互作用。当没有侧信息时，该模型可用于半监督学习，实现鲁棒的序列分类。实验结果表明该方法在序列数据的预测和分类性能方面取得了最先进的成果，并且具有控制风格转换和融合的能力。