Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
Nat Rev Genet. 2019 Jul;20(7):389-403. doi: 10.1038/s41576-019-0122-6.
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.
作为一门数据驱动的科学，基因组学在很大程度上利用机器学习来捕捉数据中的依赖关系，并得出新的生物学假设。然而，要从指数级增长的基因组学数据量中提取新的见解，就需要更具表现力的机器学习模型。通过有效地利用大型数据集，深度学习已经改变了计算机视觉和自然语言处理等领域。现在，它正成为许多基因组学建模任务的首选方法，包括预测遗传变异对基因调控机制（如 DNA 可及性和剪接）的影响。
