Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features.
Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features.
复制标题
通过异构生物医学和临床特征进行知识发现的关系网络
DOI：
10.1038/srep29915
复制
发表时间：
2016-07-18
影响因子：
4.6
通讯作者：
Zhou X
中科院分区：
文献类型：
--
作者：
Chen H;Chen W;Liu C;Zhang L;Su J;Zhou X
作为一个整体，生物医学大数据涵盖了许多特征，而每个数据集都具体描述了其中的一部分。跨异构数据源的“全特征谱”知识发现仍然是一个重大挑战。我们开发了一种称为引导的方法，用于成对关联分析的统一特征关联测量（BUFAM），以及用于跨乳腺癌队列特征的全局模块检测的关系依赖网络（RDN）建模。使用来自维克森林浸信会医疗中心电子病历的数据交叉验证发现的知识，并使用BioCarta信号签名进行注释。通过对患者的药物反应进行分层，展示了所发现模块的临床潜力。一系列发现的关联为乳腺癌提供了新的见解，例如患者的文化背景对外科手术偏好的影响。我们还发现了两组高度相关的特征，HER2和ER模块，每一组都描述了表型如何与分子特征、诊断特征和临床决策相关。发现的“ER模块”，这是占主导地位的癌症免疫，被用作一个例子，病人分层和预测药物反应，他莫昔芬和化疗。BUFAM衍生的RDN建模展示了在高度异质的生物医学大数据集上发现有临床意义和可操作知识的独特能力。
Biomedical big data, as a whole, covers numerous features, while each dataset specifically delineates part of them. “Full feature spectrum” knowledge discovery across heterogeneous data sources remains a major challenge. We developed a method called bootstrapping for unified feature association measurement (BUFAM) for pairwise association analysis, and relational dependency network (RDN) modeling for global module detection on features across breast cancer cohorts. Discovered knowledge was cross-validated using data from Wake Forest Baptist Medical Center’s electronic medical records and annotated with BioCarta signaling signatures. The clinical potential of the discovered modules was exhibited by stratifying patients for drug responses. A series of discovered associations provided new insights into breast cancer, such as the effects of patient’s cultural background on preferences for surgical procedure. We also discovered two groups of highly associated features, the HER2 and the ER modules, each of which described how phenotypes were associated with molecular signatures, diagnostic features, and clinical decisions. The discovered “ER module”, which was dominated by cancer immunity, was used as an example for patient stratification and prediction of drug responses to tamoxifen and chemotherapy. BUFAM-derived RDN modeling demonstrated unique ability to discover clinically meaningful and actionable knowledge across highly heterogeneous biomedical big data sets.