Generative models provide a well‐established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural language processing, these models have been shown to effectively summarize the complexity that underlies many types of data and enable a range of applications including supervised learning tasks, such as assigning labels to images; unsupervised learning tasks, such as dimensionality reduction; and out‐of‐sample generation, such as de novo image synthesis. With this early success, the power of generative models is now being increasingly leveraged in molecular biology, with applications ranging from designing new molecules with properties of interest to identifying deleterious mutations in our genomes and to dissecting transcriptional variability between single cells. In this review, we provide a brief overview of the technical notions behind generative models and their implementation with deep learning techniques. We then describe several different ways in which these models can be utilized in practice, using several recent applications in molecular biology as examples.

中文翻译：

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通过深度生成模型增强分子生物学的科学发现


生成模型提供了一个完善的统计框架，用于评估不确定性并从大型数据集中得出结论，特别是在存在噪声、稀疏性和偏差的情况下。这些模型最初是为计算机视觉和自然语言处理而开发的，已被证明可以有效地总结多种类型数据背后的复杂性，并支持一系列应用，包括监督学习任务，例如为图像分配标签；无监督学习任务，例如降维；以及样本外生成，例如从头图像合成。凭借这一早期成功，生成模​​型的力量现在在分子生物学中得到越来越多的利用，其应用范围从设计具有感兴趣特性的新分子到识别基因组中的有害突变以及剖析单细胞之间的转录变异性。在这篇综述中，我们简要概述了生成模型背后的技术概念及其通过深度学习技术的实现。然后，我们以最近在分子生物学中的几个应用为例，描述了这些模型在实践中应用的几种不同方式。