Data from: The implicitome: a resource for rationalizing gene-disease associations

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.高通量实验方法（如医学测序与全基因组关联研究（Genome-Wide Association Studies, GWAS））正不断识别出数量愈发庞大的遗传变异与疾病之间的潜在关联。鉴于生物复杂性（潜在基因-疾病关联多达数百万条）与数据产出速率的持续加快，亟需开发计算方法，以对潜在基因-疾病关联开展优先级排序与合理化阐释。本研究采用概念轮廓技术，从生物医学文献中挖掘出两类基因-疾病关联：一类为明确表述的显式关联组（explicitome），另一类则是规模更为庞大的隐含关联组（implicitome）。这些隐含关联大多不为原始文献作者所知，甚至并非作者有意提出，但它们极大拓展了现有生物医学知识的覆盖范围，助力基因-疾病关联的识别与解读。隐含关联组可与实验数据资源结合使用，以对已知及全新的关联进行合理化阐释。本研究通过对包括全基因组关联研究（GWAS）在内的已知与全新基因-疾病关联开展合理化阐释，验证了隐含关联组的应用价值。为便于隐含基因-疾病关联的复用，本研究遵循FAIR数据出版（FAIR Data Publishing）规范[https://www.force11.org/group/fairgroup]，采用纳米出版物（nanopublications）形式发布相关数据。此外，团队还提供了一款在线工具（http://knowledge.bio），可结合其他生物医学关联场景，对已确认及潜在的基因-疾病关联进行探索分析。