With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.

中文翻译：

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机器学习、肾脏和基因型-表型分析。

随着生物医学研究向数据丰富的科学转变，机器学习为从大规模生物数据集中提取知识提供了一个强大的工具包。全面的肾脏组学纲要（转录组学、蛋白质组学、代谢组学和基因组测序）以及其他数据模式（如电子健康记录、数字肾病理学存储库和放射学肾脏图像）的可用性越来越高，使得机器学习方法对于分析人类越来越重要肾脏数据集。在这里，我们讨论了机器学习方法如何应用于肾脏疾病的研究，特别关注如何使用它们来理解基因型和表型之间的关系。