Interdisciplinary Center for Network Science and Applications, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, United States of America.
PLoS One. 2011;6(7):e22670. doi: 10.1371/journal.pone.0022670. Epub 2011 Jul 29.
The availability of electronic health care records is unlocking the potential for novel studies on understanding and modeling disease co-morbidities based on both phenotypic and genetic data. Moreover, the insurgence of increasingly reliable phenotypic data can aid further studies on investigating the potential genetic links among diseases. The goal is to create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyze disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide novel insight into the interplay between genetics and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method, showing that disease co-morbidity can enhance our currently limited knowledge of genetic association. Furthermore, our methods for integrated networks of diverse data are widely applicable and can provide novel advances for many problems in systems biology and personalized medicine.
电子医疗记录的可用性正在为基于表型和遗传数据理解和建模疾病共病的新型研究解锁潜力。此外，越来越可靠的表型数据的出现可以帮助进一步研究疾病之间的潜在遗传联系。目标是创建一个反馈循环，其中计算工具指导和促进研究，从而提高生物学知识和临床标准，这反过来又应该生成更好的数据。我们基于从一家地区医院收集的超过 12 年的先前遗传关联研究和患者病史数据构建和分析疾病相互作用网络。通过探索这两个疾病数据层次之间的个体和组合相互作用，我们提供了对遗传学和临床现实之间相互作用的新见解。我们的结果表明遗传关系的明确定义结构和混乱的共病网络之间存在显著差异，但也突出了明显的相互依存关系。我们通过提出一种新颖的多关系链接预测方法来证明这些依赖关系的强大功能，表明疾病共病可以增强我们目前对遗传关联的有限了解。此外，我们用于多样化数据的综合网络的方法具有广泛的适用性，可以为系统生物学和个性化医学中的许多问题提供新的进展。
