Discovering disease-disease associations by fusing systems-level molecular data.

Discovering disease-disease associations by fusing systems-level molecular data.
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DOI:
10.1038/srep03202
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发表时间:
2013-11-15
期刊:
影响因子:
4.6
通讯作者:
Przulj, Natasa
Przulj, Natasa
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Zitnik, Marinka;Janjic, Vuk;Larminie, Chris;Zupan, Blaz;Przulj, Natasa

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基因组规模的遗传和基因组研究的出现使人们对疾病分类有了新的认识。最近,一种转变发生了,从简单地基于它们的共享基因将疾病联系起来,转向了分子数据的系统级整合。在这里,我们的目标是基于融合所有可用的分子相互作用和本体数据的证据来发现疾病之间的关系。我们提出了一种与现有疾病分类显著重叠的疾病分类的多层次层次结构。在这篇文章中,我们发现了目前在疾病本体中没有出现的14种疾病-疾病关联,并通过共病数据和文献整理为它们的关系提供了证据。有趣的是,尽管目前已知的人类基因相互作用的数量非常少,但我们发现它们是疾病之间联系的最重要的预测因子。最后,我们表明,遗漏任何一个包含的数据源都会降低预测质量,进一步强调了向系统级数据融合范式转变的重要性。
The advent of genome-scale genetic and genomic studies allows new insight into disease classification. Recently, a shift was made from linking diseases simply based on their shared genes towards systems-level integration of molecular data. Here, we aim to find relationships between diseases based on evidence from fusing all available molecular interaction and ontology data. We propose a multi-level hierarchy of disease classes that significantly overlaps with existing disease classification. In it, we find 14 disease-disease associations currently not present in Disease Ontology and provide evidence for their relationships through comorbidity data and literature curation. Interestingly, even though the number of known human genetic interactions is currently very small, we find they are the most important predictor of a link between diseases. Finally, we show that omission of any one of the included data sources reduces prediction quality, further highlighting the importance in the paradigm shift towards systems-level data fusion.
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