In biomedical applications of machine learning, relevant information often has a rich structure that is not easily encoded as real-valued predictors. Examples of such data include DNA or RNA sequences, gene sets or pathways, gene interaction or coexpression networks, ontologies, and phylogenetic trees. We highlight recent examples of machine learning models that use structure to constrain model architecture or incorporate structured data into model training. For machine learning in biomedicine, where sample size is limited and model interpretability is crucial, incorporating prior knowledge in the form of structured data can be particularly useful. The area of research would benefit from performant open source implementations and independent benchmarking efforts.

中文翻译：

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将生物结构整合到生物医学的机器学习模型中。

在机器学习的生物医学应用中，相关信息通常具有丰富的结构，不容易将其编码为实值预测变量。此类数据的示例包括DNA或RNA序列，基因集或途径，基因相互作用或共表达网络，本体论和系统发育树。我们重点介绍机器学习模型的最新示例，这些示例使用结构来约束模型体系结构或将结构化数据合并到模型训练中。对于生物医学中的机器学习而言，样本量有限且模型的可解释性至关重要，因此以结构化数据的形式合并先验知识可能特别有用。研究领域将受益于高性能的开源实施和独立的基准测试工作。