Choi Sanghyuk Roy, Lee Minhyeok
School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea.
Biology (Basel). 2023 Jul 22;12(7):1033. doi: 10.3390/biology12071033.
The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.
深度学习的出现和快速发展，特别是基于Transformer的架构和注意力机制，已经在包括生物信息学和基因组数据分析在内的多个领域产生了变革性影响。基因组序列与语言文本的相似性使得在从自然语言处理到基因组数据等领域取得成功的技术得以应用。本综述对Transformer架构和注意力机制在基因组和转录组数据应用中的最新进展进行了全面分析。本综述的重点是对这些技术进行批判性评估，讨论它们在基因组数据分析背景下的优缺点。随着深度学习方法的快速发展，持续评估和反思该研究的现状和未来方向变得至关重要。因此，本综述旨在为经验丰富的研究人员和新手提供及时的资源，全面展示近期进展并阐明该领域的前沿应用。此外，本综述通过批判性评估2019年至2023年的研究，突出了未来潜在的研究领域，从而为进一步的研究工作奠定基础。
