基因组学正在向数据驱动的科学发展。通过人类基因组学中高通量数据生成技术的出现,我们被一堆基因组数据淹没了。为了从基因组数据中提取知识和模式,人工智能,特别是深度学习方法一直很有帮助。在当前的审查中,我们致力于在人类基因组学的不同子领域开发和应用深度学习方法/模型。我们通过深度学习技术评估了基因组学的过度和不足领域。在本综述的后面部分已经简要讨论了基因组工具基础的深度学习算法。最后,我们简要讨论了深度学习工具在基因组中的后期应用。最后,这篇评论对于生物技术或基因组科学家来说是及时的,以指导他们为什么,何时以及如何使用深度学习方法来分析人类基因组数据。
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current
review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this
review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this
review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.