An Overview of Deep Generative Models in Functional and Evolutionary Genomics.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-05-03 DOI:10.1146/annurev-biodatasci-020722-115651
Burak Yelmen, Flora Jay
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引用次数: 0

Abstract

Following the widespread use of deep learning for genomics, deep generative modeling is also becoming a viable methodology for the broad field. Deep generative models (DGMs) can learn the complex structure of genomic data and allow researchers to generate novel genomic instances that retain the real characteristics of the original dataset. Aside from data generation, DGMs can also be used for dimensionality reduction by mapping the data space to a latent space, as well as for prediction tasks via exploitation of this learned mapping or supervised/semi-supervised DGM designs. In this review, we briefly introduce generative modeling and two currently prevailing architectures, we present conceptual applications along with notable examples in functional and evolutionary genomics, and we provide our perspective on potential challenges and future directions.

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功能和进化基因组学中的深度生成模型概述。
随着深度学习在基因组学领域的广泛应用,深度生成模型也正在成为这一广泛领域的可行方法。深度生成模型(DGM)可以学习基因组数据的复杂结构,使研究人员能够生成保留原始数据集真实特征的新型基因组实例。除了生成数据,DGM 还可以通过将数据空间映射到潜在空间来降低维度,以及通过利用学习到的映射或监督/半监督 DGM 设计来完成预测任务。在这篇综述中,我们简要介绍了生成建模和目前流行的两种架构,介绍了功能基因组学和进化基因组学中的概念应用和著名实例,并对潜在挑战和未来方向提出了自己的看法。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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