基于表型本体的诱导预测疾病-基因关联：INDIGENA
INDIGENA: inductive prediction of disease-gene associations using phenotype ontologies
AI总结：
INDIGENA通过结合表型本体和图嵌入方法，实现了对新疾病表型的诱导预测，提升了基因-疾病关联预测的性能。
摘要：
动机：预测基因-疾病关联（GDAs）的问题是确定哪些基因与疾病相关。GDAs预测可以被框架为一个排序问题，其中基于特征如表型相似性对基因进行排序。通过使用表型本体描述表型，可以使用基于本体的语义相似性度量。然而，传统语义相似性度量仅使用本体的层级结构。最近基于本体嵌入的方法在潜在空间中比较表型；这些方法可以使用所有本体公理以及监督信号，但本质上是转换性的，即查询实体必须在学习嵌入时已知，因此这些方法在推理时无法推广到新的疾病（一组表型）。结果：我们开发了INDIGENA，一种用于基于表型集排序基因的诱导疾病-基因关联方法。我们的方法首先使用图投影将表型本体的公理映射到图结构中，然后使用图嵌入创建表型的潜在表示。我们使用显式聚合策略将表型嵌入合并到基因或疾病的表示中，使我们能够推广到新的表型集。我们还开发了一种方法，使表型嵌入和相似性度量任务特定化，通过包含来自已知基因-疾病关联的监督信号。我们应用我们的方法到小鼠模型中的人类疾病，并证明我们能够显著优于诱导语义相似性基线度量，并在预测基因-疾病关联时达到与转换方法相似的性能，同时更具通用性。可用性与实现：https://github.com/bio-ontology-research-group/indigena
英文摘要：
Motivation: Predicting gene-disease associations (GDAs) is the problem to determine which gene is associated with a disease. GDA prediction can be framed as a ranking problem where genes are ranked for a query disease, based on features such as phenotypic similarity. By describing phenotypes using phenotype ontologies, ontology-based semantic similarity measures can be used. However, traditional semantic similarity measures use only the ontology taxonomy. Recent methods based on ontology embeddings compare phenotypes in latent space; these methods can use all ontology axioms as well as a supervised signal, but are inherently transductive, i.e., query entities must already be known at the time of learning embeddings, and therefore these methods do not generalize to novel diseases (sets of phenotypes) at inference time. Results: We developed INDIGENA, an inductive disease-gene association method for ranking genes based on a set of phenotypes. Our method first uses a graph projection to map axioms from phenotype ontologies to a graph structure, and then uses graph embeddings to create latent representations of phenotypes. We use an explicit aggregation strategy to combine phenotype embeddings into representations of genes or diseases, allowing us to generalize to novel sets of phenotypes. We also develop a method to make the phenotype embeddings and the similarity measure task-specific by including a supervised signal from known gene-disease associations. We apply our method to mouse models of human disease and demonstrate that we can significantly improve over the inductive semantic similarity baseline measures, and reach a performance similar to transductive methods for predicting gene-disease associations while being more general. Availability and Implementation: https://github.com/bio-ontology-research-group/indigena
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