Abstract：
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.
摘要：
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。传统的变化检测方法依赖于人工检查或预定义的规则，这可能是耗时且容易出错的。因此,基于深度学习的变异检测方法由于能够自动学习区分变异的基因组特征而受到关注。在我们的审查中,我们讨论了基于深度学习的算法的最新进展，用于检测基因组数据中的小变化和结构变化，以及它们的优点和局限性。
