School of Computer Science & Technology, Dalian University of Technology, 116024, Dalian, China.
CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China.
Sci Rep. 2017 Oct 30;7(1):14339. doi: 10.1038/s41598-017-14682-5.
Analyzing omics data from a network-based perspective can facilitate biomarker discovery. To improve disease diagnosis and identify prospective information indicating the onset of complex disease, a computational method for identifying potential biomarkers based on differential sub-networks (PB-DSN) is developed. In PB-DSN, Pearson correlation coefficient (PCC) is used to measure the relationship between feature ratios and to infer potential networks. A differential sub-network is extracted to identify crucial information for discriminating different groups and indicating the emergence of complex diseases. Subsequently, PB-DSN defines potential biomarkers based on the topological analysis of these differential sub-networks. In this study, PB-DSN is applied to handle a static genomics dataset of small, round blue cell tumors and a time-series metabolomics dataset of hepatocellular carcinoma. PB-DSN is compared with support vector machine-recursive feature elimination, multivariate empirical Bayes statistics, analyzing time-series data based on dynamic networks, molecular networks based on PCC, PinnacleZ, graph-based iterative group analysis, KeyPathwayMiner and BioNet. The better performance of PB-DSN not only demonstrates its effectiveness for the identification of discriminative features that facilitate disease classification, but also shows its potential for the identification of warning signals.
从网络角度分析组学数据有助于发现生物标志物。为了改善疾病诊断并识别预示复杂疾病发生的前瞻性信息，开发了一种基于差异子网络（PB-DSN）的潜在生物标志物识别计算方法。在 PB-DSN 中，皮尔逊相关系数（PCC）用于测量特征比之间的关系，并推断潜在网络。提取差异子网络以识别区分不同组和指示复杂疾病出现的关键信息。随后，PB-DSN 根据这些差异子网络的拓扑分析来定义潜在的生物标志物。在这项研究中，PB-DSN 应用于处理小圆形蓝色细胞肿瘤的静态基因组数据集和肝细胞癌的时间序列代谢组学数据集。将 PB-DSN 与支持向量机递归特征消除、多元经验贝叶斯统计、基于动态网络的时间序列数据分析、基于 PCC 的分子网络、PinnacleZ、基于图的迭代分组分析、KeyPathwayMiner 和 BioNet 进行比较。PB-DSN 更好的性能不仅证明了其在识别有助于疾病分类的有区别特征方面的有效性，还展示了其在识别预警信号方面的潜力。
