生物
变化(天文学)
深度学习
任务(项目管理)
个性化医疗
机器学习
计算生物学
精密医学
领域(数学)
基因组学
基因组
人工智能
数据科学
生物信息学
计算机科学
遗传学
基因
物理
数学
管理
天体物理学
纯数学
经济
作者
Ren Junjun,Zhang Zhengqian,Wu Ying,Jialiang Wang,Yongzhuang Liu
出处
期刊:Briefings in Functional Genomics
[Oxford University Press]
日期:2024-02-16
摘要
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.
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