Exploring and exploiting disease interactions from multi-relational gene and phenotype networks.
Exploring and exploiting disease interactions from multi-relational gene and phenotype networks.
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从多关系基因和表型网络中探索和利用疾病相互作用。
DOI：
10.1371/journal.pone.0022670
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Chawla NV
中科院分区：
文献类型：
--
作者：
Davis DA;Chawla NV
电子医疗保健记录的可用性正在释放基于表型和遗传数据理解和建模疾病共病的新研究的潜力。此外，越来越可靠的表型数据的涌现可以帮助进一步研究疾病之间的潜在遗传联系。其目标是创建一个反馈回路，其中计算工具指导和促进研究，从而改善生物学知识和临床标准，从而产生更好的数据。我们建立和分析疾病相互作用网络的基础上收集的数据，从以前的遗传关联研究和患者的病史，跨越12年，从地区医院获得。通过探索这两个层面的疾病数据之间的个体和组合相互作用，我们对遗传学和临床现实之间的相互作用提供了新的见解。我们的研究结果表明，遗传关系的明确定义的结构和混乱的共病网络之间存在显着差异，但也突出了明确的相互依赖性。我们通过提出一种新的多关系链接预测方法来证明这些依赖关系的力量，表明疾病共病可以增强我们目前对遗传关联的有限知识。此外，我们的方法集成网络的不同数据是广泛适用的，可以提供新的进展，在系统生物学和个性化医疗的许多问题。
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.
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影响因子：
12.3
作者：
Baudot A;Gómez-López G;Valencia A
通讯作者：
Valencia A
影响因子：
4.4
作者：
HOLLAND, PW;LEINHARD.S
通讯作者：
LEINHARD.S
DOI：
10.1073/pnas.0601602103
发表时间：
2006-06-06
影响因子：
11.1
作者：
Newman, M. E. J.
通讯作者：
Newman, M. E. J.
DOI：
10.1093/bioinformatics/btq675
发表时间：
2011-02-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Smoot ME;Ono K;Ruscheinski J;Wang PL;Ideker T
通讯作者：
Ideker T
DOI：
10.1093/bioinformatics/btp213
发表时间：
2009-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Ucar D;Beyer A;Parthasarathy S;Workman CT
通讯作者：
Workman CT