Shoshi Alban, Hofestädt Ralf, Zolotareva Olga, Friedrichs Marcel, Maier Alex, Ivanisenko Vladimir A, Dosenko Victor E, Bragina Elena Yu
Bielefeld University, Bioinformatics/Medical Informatics Department, Bielefeld, Germany.
Bielefeld University, International Research Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Bielefeld, Germany.
J Integr Bioinform. 2018 Dec 25;15(4):/j/jib.2018.15.issue-4/jib-2018-0049/jib-2018-0049.xml. doi: 10.1515/jib-2018-0049.
The prevalence of comorbid diseases poses a major health issue for millions of people worldwide and an enormous socio-economic burden for society. The molecular mechanisms for the development of comorbidities need to be investigated. For this purpose, a workflow system was developed to aggregate data on biomedical entities from heterogeneous data sources. The process of integrating and merging all data sources of the workflow system was implemented as a semi-automatic pipeline that provides the import, fusion, and analysis of the highly connected biomedical data in a Neo4j database GenCoNet. As a starting point, data on the common comorbid diseases essential hypertension and bronchial asthma was integrated. GenCoNet (https://genconet.kalis-amts.de) is a curated database that provides a better understanding of hereditary bases of comorbidities.
共病的流行给全球数百万人带来了重大的健康问题，也给社会造成了巨大的社会经济负担。需要研究共病发生发展的分子机制。为此，开发了一个工作流程系统，用于汇总来自异构数据源的生物医学实体数据。工作流程系统的所有数据源的集成和合并过程被实现为一个半自动管道，该管道在Neo4j数据库GenCoNet中提供对高度关联的生物医学数据的导入、融合和分析。作为起点，整合了常见共病原发性高血压和支气管哮喘的数据。GenCoNet（https://genconet.kalis-amts.de）是一个经过整理的数据库，有助于更好地理解共病的遗传基础。
