With advances in genomic sequencing technology, a large amount of data is publicly available for the research community to extract meaningful and reliable associations among risk genes and the mechanisms of disease. However, this exponential growth of data is spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality what hinders the differentiation of clinically valid relationships from those that are less well-sustained and that could lead to wrong diagnosis. This paper presents how conceptual models can play a key role to efficiently manage genomic data. These data must be accessible, informative and reliable enough to extract valuable knowledge in the context of the identification of evidence supporting the relationship between DNA variants and disease. The approach presented in this paper provides a solution that help researchers to organize, store and process information focusing only on the data that are relevant and minimizing the impact that the information overload has in clinical and research contexts. A case-study (epilepsy) is also presented, to demonstrate its application in a real context.

中文翻译：

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使用概念建模改进基因组数据管理。

随着基因组测序技术的进步，研究界可以公开获得大量数据，以提取风险基因与疾病机制之间有意义且可靠的关联。然而，这种指数增长的数据分布在数千个异构存储库中，以多种格式和不同的质量水平表示，这阻碍了将临床有效关系与那些持续性较​​差并可能导致错误诊断的关系区分开来。本文介绍了概念模型如何在有效管理基因组数据方面发挥关键作用。这些数据必须是可访问的、信息丰富的且足够可靠，以便在识别支持 ​​DNA 变异与疾病之间关系的证据的背景下提取有价值的知识。本文提出的方法提供了一种解决方案，可帮助研究人员组织、存储和处理信息，只关注相关数据，并最大限度地减少信息过载对临床和研究环境的影响。还提供了一个案例研究（癫痫），以展示其在真实环境中的应用。