Liu Ting, Pan Xueli, Wang Xu, Feenstra K Anton, Heringa Jaap, Huang Zhisheng
Knowledge Representation and Reasoning (KR&R) Group, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Center for Integrative Bioinformatics VU (IBIVU), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Health Inf Sci Syst. 2020 Nov 24;9(1):3. doi: 10.1007/s13755-020-00128-2. eCollection 2021 Dec.
Gut microbiota produce and modulate the production of neurotransmitters which have been implicated in mental disorders. Neurotransmitters may act as 'matchmaker' between gut microbiota imbalance and mental disorders. Most of the relevant research effort goes into the relationship between gut microbiota and neurotransmitters and the other between neurotransmitters and mental disorders, while few studies collect and analyze the dispersed research results in systematic ways. We therefore gather the dispersed results that in the existing studies into a structured knowledge base for identifying and predicting the potential relationships between gut microbiota and mental disorders. In this study, we propose to construct a gut microbiota knowledge graph for mental disorder, which named as MiKG4MD. It is extendable by linking to future ontologies by just adding new relationships between existing information and new entities. This extendibility is emphasized for the integration with existing popular ontologies/terminologies, e.g. UMLS, MeSH, and KEGG. We demonstrate the performance of MiKG4MD with three SPARQL query test cases. Results show that the MiKG4MD knowledge graph is an effective method to predict the relationships between gut microbiota and mental disorders.
肠道微生物群产生并调节神经递质的产生，而神经递质与精神障碍有关。神经递质可能充当肠道微生物群失衡与精神障碍之间的“媒人”。大多数相关研究工作集中在肠道微生物群与神经递质之间的关系以及神经递质与精神障碍之间的关系上，而很少有研究以系统的方式收集和分析分散的研究结果。因此，我们将现有研究中的分散结果收集到一个结构化知识库中，以识别和预测肠道微生物群与精神障碍之间的潜在关系。在本研究中，我们提议构建一个用于精神障碍的肠道微生物群知识图谱，名为MiKG4MD。通过在现有信息和新实体之间添加新关系，它可以通过链接到未来的本体进行扩展。这种可扩展性对于与现有流行本体/术语（如UMLS、MeSH和KEGG）的集成非常重要。我们用三个SPARQL查询测试用例展示了MiKG4MD的性能。结果表明，MiKG4MD知识图谱是预测肠道微生物群与精神障碍之间关系的有效方法。
