Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA.
Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany.
Hum Mol Genet. 2018 May 1;27(R1):R63-R71. doi: 10.1093/hmg/ddy115.
The human genome is now investigated through high-throughput functional assays, and through the generation of population genomic data. These advances support the identification of functional genetic variants and the prediction of traits (e.g. deleterious variants and disease). This review summarizes lessons learned from the large-scale analyses of genome and exome data sets, modeling of population data and machine-learning strategies to solve complex genomic sequence regions. The review also portrays the rapid adoption of artificial intelligence/deep neural networks in genomics; in particular, deep learning approaches are well suited to model the complex dependencies in the regulatory landscape of the genome, and to provide predictors for genetic variant calling and interpretation.
人类基因组现在通过高通量功能检测和群体基因组数据的产生进行研究。这些进展支持了功能遗传变异体的鉴定和性状（例如有害变异体和疾病）的预测。这篇综述总结了从大规模基因组和外显子数据集分析、群体数据建模和机器学习策略解决复杂基因组序列区域中吸取的经验教训。该综述还描述了人工智能/深度神经网络在基因组学中的快速采用；特别是，深度学习方法非常适合模拟基因组调控景观中的复杂依赖关系，并为遗传变异体的调用和解释提供预测器。
