Experimental approaches to study tissue specificity enable insight into the nature and organization of the cell types and tissues that constitute complex multicellular organisms. Machine learning provides a powerful tool to investigate and interpret tissue-specific experimental data. In this Review, we first provide a brief introduction to key single-cell and whole-tissue approaches that allow investigation of tissue specificity and then highlight two classes of machine-learning-based methods, which can be applied to analyse, model and interpret these experimental data. Deep learning methods can predict tissue-dependent effects of individual mutations on gene expression, alternative splicing and disease phenotypes. Network-based approaches can capture relationships between biomolecules, integrate large heterogeneous data compendia to model molecular circuits and identify tissue-specific functional relationships and regulatory connections. We conclude with an outlook to future possibilities in examining multicellular complexity by combining high-resolution, large-scale multiomics data sets and interpretable machine learning models.

研究组织特异性的实验方法可以深入了解构成复杂多细胞生物的细胞类型和组织的性质和组织。机器学习提供了一种强大的工具来研究和解释组织特异性实验数据。在这篇综述中，我们首先简要介绍了允许研究组织特异性的关键单细胞和全组织方法，然后重点介绍两类基于机器学习的方法，它们可用于分析、建模和解释这些方法实验数据。深度学习方法可以预测个体突变对基因表达、选择性剪接和疾病表型的组织依赖性影响。基于网络的方法可以捕捉生物分子之间的关系，整合大型异构数据纲要以模拟分子回路并识别组织特异性功能关系和调节连接。最后，我们结合高分辨率、大规模多组学数据集和可解释的机器学习模型，展望了检查多细胞复杂性的未来可能性。