Enhancing scientific discoveries in molecular biology with deep generative models.
Enhancing scientific discoveries in molecular biology with deep generative models.
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DOI：
10.15252/msb.20199198
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发表时间：
2020-09
影响因子：
9.9
通讯作者：
Yosef N
中科院分区：
文献类型：
--
作者：
Lopez R;Gayoso A;Yosef N
生成模型为评估不确定性以及从大型数据集中得出结论提供了一个完善的统计框架，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，已被证明能有效概括许多类型数据背后的复杂性，并使一系列应用成为可能，包括监督学习任务，例如给图像标注；无监督学习任务，例如降维；以及样本外生成，例如从头进行图像合成。随着这些早期的成功，生成模型的威力现在在分子生物学中越来越多地得到利用，其应用范围从设计具有感兴趣特性的新分子，到识别我们基因组中的有害突变，再到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以及它们通过深度学习技术的实现。然后，我们以分子生物学中的几个近期应用为例，描述了这些模型在实践中可以被利用的几种不同方式。
这篇综述介绍了深度生成模型以及它们通过深度学习技术的实现。介绍了在基因组学和单细胞生物学中的几个应用，并讨论了此类神经网络模型的可解释性。
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. This Review provides an introduction into deep generative models and their implementation with deep learning techniques. Several applications in genomics and single‐cell biology are presented, and the interpretability of such neural network models is discussed.
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影响因子：
48
作者：
Amodio, Matthew;van Dijk, David;Srinivasan, Krishnan;Chen, William S.;Mohsen, Hussein;Moon, Kevin R.;Campbell, Allison;Zhao, Yujiao;Wang, Xiaomei;Venkataswamy, Manjunatha;Desai, Anita;Ravi, V.;Kumar, Priti;Montgomery, Ruth;Wolf, Guy;Krishnaswamy, Smita
通讯作者：
Krishnaswamy, Smita
影响因子：
--
作者：
Andrews, Tallulah S;Hemberg, Martin
通讯作者：
Hemberg, Martin
DOI：
10.1146/annurev-statistics-022513-115657
发表时间：
2014-01-01
期刊：
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 1
影响因子：
--
作者：
Blei, David M.
通讯作者：
Blei, David M.
影响因子：
14.9
作者：
Bento AP;Gaulton A;Hersey A;Bellis LJ;Chambers J;Davies M;Krüger FA;Light Y;Mak L;McGlinchey S;Nowotka M;Papadatos G;Santos R;Overington JP
通讯作者：
Overington JP
DOI：
10.1161/circoutcomes.118.005122
发表时间：
2019-07-01
影响因子：
6.9
作者：
Beaulieu-Jones, Brett K.;Wu, Zhiwei Steven;Greene, Casey S.
通讯作者：
Greene, Casey S.