Individual-specific edge-network analysis for disease prediction.
Individual-specific edge-network analysis for disease prediction.
复制标题
用于疾病预测的个体特定边缘网络分析
DOI：
10.1093/nar/gkx787
复制
发表时间：
2017-11-16
影响因子：
14.9
通讯作者：
Chen L
中科院分区：
文献类型：
--
作者：
Yu X;Zhang J;Sun S;Zhou X;Zeng T;Chen L
摘要预测疾病前状态或健康不可逆恶化的临界点是一项困难的任务。结合动态网络生物标志物（DNB）理论的边网络分析（ENA），通过挖掘组学数据中丰富的动态和高维信息，为研究这一问题开辟了新的途径。虽然理论上ENA具有在疾病进展期间识别疾病前状态的能力，但它需要对每个个体进行多个样本的这种预测，这在临床实践中通常是不可用的，从而限制了其在个性化医疗中的应用。在这项工作中，以克服这个问题，我们提出了个人特定的ENA（iENA）与DNB确定疾病前的状态，每个人在一个单一的样本的方式。特别是，除了传统的疾病诊断外，iENA还可以识别用于疾病预测的个体特异性生物标志物。为了证明其有效性，将iENA应用于H3N2队列组学数据的分析，并成功地准确地检测出每个个体在发生时间和事件上的流感感染预警信号，实际上实现了AUC大于0.9。iENA不仅发现了新的个体特异性生物标志物，而且还恢复了以前工作中报道的流感感染的常见生物标志物。此外，iENA还检测了具有显著边缘生物标志物的多种癌症的关键阶段，并通过TCGA数据和其他独立数据的生存分析进一步验证。
Abstract Predicting pre-disease state or tipping point just before irreversible deterioration of health is a difficult task. Edge-network analysis (ENA) with dynamic network biomarker (DNB) theory opens a new way to study this problem by exploring rich dynamical and high-dimensional information of omics data. Although theoretically ENA has the ability to identify the pre-disease state during the disease progression, it requires multiple samples for such prediction on each individual, which are generally not available in clinical practice, thus limiting its applications in personalized medicine. In this work to overcome this problem, we propose the individual-specific ENA (iENA) with DNB to identify the pre-disease state of each individual in a single-sample manner. In particular, iENA can identify individual-specific biomarkers for the disease prediction, in addition to the traditional disease diagnosis. To demonstrate the effectiveness, iENA was applied to the analysis on omics data of H3N2 cohorts and successfully detected early-warning signals of the influenza infection for each individual both on the occurred time and event in an accurate manner, which actually achieves the AUC larger than 0.9. iENA not only found the new individual-specific biomarkers but also recovered the common biomarkers of influenza infection reported from previous works. In addition, iENA also detected the critical stages of multiple cancers with significant edge-biomarkers, which were further validated by survival analysis on both TCGA data and other independent data.
登录
查看更多内容
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
使用样本特定网络对疾病进行个性化表征
影响因子：
14.9
作者：
Liu X;Wang Y;Ji H;Aihara K;Chen L
通讯作者：
Chen L
关于癌症基因组地图集的泛伴奏蛋白质组学观点。
影响因子：
16.6
作者：
Akbani, Rehan;Ng, Patrick Kwok Shing;Werner, Henrica M. J.;Shahmoradgoli, Maria;Zhang, Fan;Ju, Zhenlin;Liu, Wenbin;Yang, Ji-Yeon;Yoshihara, Kosuke;Li, Jun;Ling, Shiyun;Seviour, Elena G.;Ram, Prahlad T.;Minna, John D.;Diao, Lixia;Tong, Pan;Heymach, John V.;Hill, Steven M.;Dondelinger, Frank;Stadler, Nicolas;Byers, Lauren A.;Meric-Bernstam, Funda;Weinstein, John N.;Broom, Bradley M.;Verhaak, Roeland G. W.;Liang, Han;Mukherjee, Sach;Lu, Yiling;Mills, Gordon B.
通讯作者：
Mills, Gordon B.
通过动态网络标记识别具有强噪声的关键转变的预警信号
影响因子：
4.6
作者：
Liu R;Chen P;Aihara K;Chen L
通讯作者：
Chen L
影响因子：
78.8
作者：
Hood, Leroy;Friend, Stephen H.
通讯作者：
Friend, Stephen H.