基于网络弹性的早期疾病预警
Early Warning of Diseases Based on Network Resilience
摘要: 疾病的发展大致可以分为三个阶段，正常状态，前疾病状态和疾病状态。前疾病状态可以根据生物网络弹性在不同阶段的动态特性识别。然而，对于网络弹性的度量，目前的研究方法大多基于模型，并且仅适用于低维度的数据，对于高通量的基因数据并不适用。在本文中，我们提出了一个数据驱动的计算基因关联网络弹性的方法，并且使用该方法识别前疾病状态。该方法的有效性已通过在1个模拟数据集和5个真实数据集的应用中得到证实。5个真实数据集包含了小鼠急性肺部损伤的基因微阵列数据集以及4个TCGA数据库的癌症数据集(肺腺癌、胃腺癌、甲状腺癌、结肠癌)。
Abstract: The progression of diseases can be roughly divided into three stages: normal state, pre-disease state and disease state. The pre-disease state could be identified according to the dynamic characteristics of biological network resilience at different stages. However, for the evaluation of network resilience, the current materials are mostly model-based and only applicable to low-dimensional data, rather than high-throughput genetic data. In this paper, we proposed a data-driven method for evaluating the resilience of gene-related networks, and used this method to identify pre-disease states. The validity of this method was proved by the application of one simulated data set and five real data sets. The five real data sets included gene microarray data sets of acute lung injury in mice and four cancer data sets from TCGA database (lung adenocarcinoma, gastric adenocarcinoma, thyroid cancer, and colon cancer).
文章引用：马硕, 刘锐. 基于网络弹性的早期疾病预警[J]. 应用数学进展, 2021, 10(2): 617-631. https://doi.org/10.12677/AAM.2021.102067

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