Sperschneider Jana
Biological Data Science Institute, The Australian National University, Canberra, ACT, 2600, Australia.
Black Mountain Science and Innovation Park, CSIRO Agriculture and Food, Canberra, ACT, 2601, Australia.
New Phytol. 2020 Oct;228(1):35-41. doi: 10.1111/nph.15771. Epub 2019 Mar 26.
Machine learning (ML) encompasses statistical methods that learn to identify patterns in complex datasets. Here, I review application areas in plant-pathogen interactions that have recently benefited from ML, such as disease monitoring, the discovery of gene regulatory networks, genomic selection for disease resistance and prediction of pathogen effectors. However, achieving robust performance from ML is not trivial and requires knowledge of both the methodology and the biology. I discuss common pitfalls and challenges in using ML approaches. Finally, I highlight future opportunities for ML as a tool for dissecting plant-pathogen interactions using high-throughput data, for example, through integration of diverse data sources and the analysis with higher resolution, such as from individual cells or on elaborate spatial and temporal scales.
机器学习（ML）涵盖了用于学习识别复杂数据集中模式的统计方法。在此，我回顾了植物 - 病原体相互作用中最近受益于机器学习的应用领域，例如疾病监测、基因调控网络的发现、抗病性的基因组选择以及病原体效应子的预测。然而，要使机器学习实现强大的性能并非易事，需要掌握方法学和生物学两方面的知识。我将讨论使用机器学习方法时常见的陷阱和挑战。最后，我强调了机器学习作为一种工具在利用高通量数据剖析植物 - 病原体相互作用方面的未来机遇，例如通过整合多样的数据源以及在更高分辨率下进行分析，如从单个细胞或在精细的时空尺度上进行分析。
