背景:了解基因相互作用对疾病表型的影响越来越被认为是遗传疾病研究的一个重要方面。这一趋势反映在对寡基因疾病的临床研究数量不断增加,其中疾病表现受到几个特定基因变异组合的影响。尽管已经开发了统计机器学习方法来识别与低基因疾病相关的相关遗传变异或基因组合,它们依赖于抽象特征和黑盒模型,对医学专家的可解释性构成挑战,并阻碍他们理解和验证预测的能力。在这项工作中,我们提出了一部小说,基于知识图的可解释预测方法,不仅可以提供对致病基因相互作用的准确预测,还可以为这些结果提供解释。
结果:我们介绍BOCK,为探索致病遗传相互作用而构建的知识图谱,将临床病例中有关寡基因疾病的策展信息与相关的生物医学网络和本体论进行整合。使用此图,我们开发了一种基于连接基因对的异质路径的新预测框架。该方法训练了一个可解释的决策集模型,该模型不仅可以准确预测致病基因的相互作用,还揭示了与这些疾病相关的模式。我们方法的一个独特方面是它能够提供,随着每一个积极的预测,以子图的形式解释,揭示导致每个致病预测的具体实体和关系。
结论:我们的方法,考虑到可解释性,利用知识图中的异质路径信息来预测致病基因的相互作用,并产生有意义的解释。这不仅拓宽了我们对寡基因疾病的分子机制的理解,而且还提出了知识图谱在为遗传研究创建更透明和有洞察力的预测因子方面的新应用。
BACKGROUND: Understanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease research. This trend is reflected by the growing amount of clinical research on oligogenic diseases, where disease manifestations are influenced by combinations of variants on a few specific genes. Although statistical machine-learning methods have been developed to identify relevant genetic variant or gene combinations associated with oligogenic diseases, they rely on abstract features and black-box models, posing challenges to interpretability for medical experts and impeding their ability to comprehend and validate predictions. In this work, we present a novel, interpretable predictive approach based on a knowledge graph that not only provides accurate predictions of disease-causing gene interactions but also offers explanations for these results.
RESULTS: We introduce BOCK, a knowledge graph constructed to explore disease-causing genetic interactions, integrating curated information on oligogenic diseases from clinical cases with relevant biomedical networks and ontologies. Using this graph, we developed a novel predictive framework based on heterogenous paths connecting gene pairs. This method trains an interpretable decision set model that not only accurately predicts pathogenic gene interactions, but also unveils the patterns associated with these diseases. A unique aspect of our approach is its ability to offer, along with each positive prediction, explanations in the form of subgraphs, revealing the specific entities and relationships that led to each pathogenic prediction.
CONCLUSIONS: Our method, built with interpretability in mind, leverages heterogenous path information in knowledge graphs to predict pathogenic gene interactions and generate meaningful explanations. This not only broadens our understanding of the molecular mechanisms underlying oligogenic diseases, but also presents a novel application of knowledge graphs in creating more transparent and insightful predictors for genetic research.