Application of Deep Learning in Plant–Microbiota Association Analysis - Deng, Zhiyu; Zhang, Jinming; Li, Junya; Zhang, Xiujun (Frontiers in Genetics, 2021)
The deep learning methods have been increasingly applied to learn the microbiome data due to their powerful strength of handling the complex, sparse, noisy, and high-dimensional data. Unraveling the association between microbiome and plant phenotype can illustrate the effect of microbiome on host and then guide the agriculture management. The applications of deep learning models in plant-associated microbiota data just break the ice. In this study, we review the analytic strategies and critical steps in the microbiome data analysis. We discuss the accessible usage of the models in plant-microbiome correlation analysis and prediction task, and clarify how the models improve the performances of the analysis pipeline. We also introduce and summarize the superiority of deep learning methods in modeling and interpretation for association research.