Harbin Institute of Technology, School of Computer Science and Technology, Harbin 150001, China.
Brief Funct Genomics. 2024 Jul 19;23(4):303-313. doi: 10.1093/bfgp/elae003.
Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.
基因组测序数据在个性化医疗和诊断领域变得越来越重要。然而，准确检测基因组变异仍然是一项具有挑战性的任务。传统的变异检测方法依赖于手动检查或预定义的规则，这可能既耗时又容易出错。因此，基于深度学习的变异检测方法因其能够自动学习区分变体的基因组特征而受到关注。在我们的综述中，我们讨论了基于深度学习的算法在检测基因组数据中小变异和结构变异方面的最新进展，以及它们的优缺点。
