MMiKG: A Knowledge Graph-based Platform for Path Mining of Microbiota-Mental Diseases Interactions

The original datasets released in MMiKG, containing relevant information like PMID of labels and relations (triples). Background: Gut microbiota has been demonstrated to be crucial in gut-brain axis. In this research, knowledge graphs was leveraged to aggregate and assimilate relevant information on...

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Hauptverfasser: Haoran Sun, Zhaoqi Son, Qiuming Chen, Meiling Wang, Furong Tang, Lijun Dou, Zou, Quan, Fenglong Yang
Format: Dataset
Sprache:eng
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Zusammenfassung:The original datasets released in MMiKG, containing relevant information like PMID of labels and relations (triples). Background: Gut microbiota has been demonstrated to be crucial in gut-brain axis. In this research, knowledge graphs was leveraged to aggregate and assimilate relevant information on the microbiome-gut-brain axis and its intricate relationships with mental diseases and formed a knowledge graph platform named MMiKG ► Advantages of MMiKG: Assist users in semantic search and visualization operations Make the scattered knowledge machine-readable and interpretable Boost users’ confidence in the accuracy of the information Support better decision-making ► What users can do with MMiKG: Integrate diverse resources Infer potential associations between gut microbiota and mental diseases Tools: MMiKG contains '770' entities and '1,257' triples among them. These items cover '20' common mental illnesses, '270' types of gut microbes, and '480' distinct intermediates. ► MMiKG's original data includes two folders: ♦ The labels folder: Present information of each entity’s 'Id', 'Name', 'Degree', 'Type' 'Disease.csv', 'Intermediate.csv', 'Microbiota.csv ♦ The relationships folder: Present information of 'SourceId', 'TargetId', 'Number', 'Reference PMID' 'Disease.csv', 'Intermediate.csv', 'Microbiota.csv'
DOI:10.5281/zenodo.8266683