Department of Bio and Brain Engineering, Korea Advanced Institutre of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea.
Bioinformatics. 2013 Aug 15;29(16):2017-23. doi: 10.1093/bioinformatics/btt327. Epub 2013 Jun 6.
Complex physiological relationships exist among human diseases. Thus, the identification of disease associations could provide new methods of disease care and diagnosis. To this end, numerous studies have investigated disease associations. However, combinatorial effect of physiological factors, which is the main characteristic of biological systems, has not been considered in most previous studies.
In this study, we inferred disease associations with a novel approach that considered disease-related clinical factors in combinatorial ways by using the National Health and Nutrition Examination Survey data, and the results have been shown as disease networks. Here, the FP-growth algorithm, an association rule mining algorithm, was used to generate a clinical attribute combination profile of each disease. In addition, we characterized the 22 clinical risk attribute combinations frequently discovered from the 26 diseases in this study. Furthermore, we validated that the results of this study have great potential for drug repositioning and outperform other existing disease networks in this regard. Finally, we suggest a few disease pairs as new candidates for drug repositioning and provide the evidence of their associations from the literature.
人类疾病之间存在复杂的生理关系。因此，识别疾病关联可以为疾病治疗和诊断提供新的方法。为此，许多研究都调查了疾病的关联。然而，在大多数先前的研究中，没有考虑生理因素的组合效应，这是生物系统的主要特征。
在这项研究中，我们通过使用国家健康和营养检查调查数据，以一种新的方法推断疾病关联，该方法以组合的方式考虑了与疾病相关的临床因素，并将结果表示为疾病网络。在这里，使用关联规则挖掘算法 FP-growth 算法为每种疾病生成临床属性组合概况。此外，我们还对从 26 种疾病中经常发现的 22 个临床风险属性组合进行了特征描述。此外，我们验证了该研究的结果在药物重定位方面具有很大的潜力，并且在这方面优于其他现有的疾病网络。最后，我们提出了一些疾病对作为药物重定位的新候选，并从文献中提供了它们关联的证据。
