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Review

Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review

School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
Mathematics 2023, 11(14), 3055; https://doi.org/10.3390/math11143055
Submission received: 26 May 2023 / Revised: 7 July 2023 / Accepted: 10 July 2023 / Published: 10 July 2023
(This article belongs to the Special Issue Big Data and Bioinformatics)

Abstract

The evolving field of generative artificial intelligence (GenAI), particularly generative deep learning, is revolutionizing a host of scientific and technological sectors. One of the pivotal innovations within this domain is the emergence of generative adversarial networks (GANs). These unique models have shown remarkable capabilities in crafting synthetic data, closely emulating real-world distributions. Notably, their application to gene expression data systems is a fascinating and rapidly growing focus area. Restrictions related to ethical and logistical issues often limit the size, diversity, and data-gathering speed of gene expression data. Herein lies the potential of GANs, as they are capable of producing synthetic gene expression data, offering a potential solution to these limitations. This review provides a thorough analysis of the most recent advancements at this innovative crossroads of GANs and gene expression data, specifically during the period from 2019 to 2023. In the context of the fast-paced progress in deep learning technologies, accurate and inclusive reviews of current practices are critical to guiding subsequent research efforts, sharing knowledge, and catalyzing continual growth in the discipline. This review, through highlighting recent studies and seminal works, serves as a key resource for academics and professionals alike, aiding their journey through the compelling confluence of GANs and gene expression data systems.
Keywords: generative adversarial networks (GAN); gene expression data; transcriptome; RNA; mRNA; deep learning; genomic data; artificial intelligence; generative artificial intelligence; genetics generative adversarial networks (GAN); gene expression data; transcriptome; RNA; mRNA; deep learning; genomic data; artificial intelligence; generative artificial intelligence; genetics

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MDPI and ACS Style

Lee, M. Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review. Mathematics 2023, 11, 3055. https://doi.org/10.3390/math11143055

AMA Style

Lee M. Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review. Mathematics. 2023; 11(14):3055. https://doi.org/10.3390/math11143055

Chicago/Turabian Style

Lee, Minhyeok. 2023. "Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review" Mathematics 11, no. 14: 3055. https://doi.org/10.3390/math11143055

APA Style

Lee, M. (2023). Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review. Mathematics, 11(14), 3055. https://doi.org/10.3390/math11143055

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