As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics.

随着群体基因组数据集规模的不断扩大，研究人员面临着理解大量信息的艰巨任务。为了跟上数据爆炸的步伐，群体遗传推断的计算方法正在迅速发展，以最好地利用基因组序列数据。在这篇综述中，我们讨论了计算群体基因组学中出现的一种新范式：监督机器学习（ML）。我们回顾了机器学习的基础知识，讨论了监督机器学习在群体遗传学中优于竞争方法的最新应用，并描述了该领域有前景的未来方向。最终，我们认为监督机器学习是一种重要且未得到充分利用的工具，在进化基因组学领域具有巨大的潜力。