Parenclitic hypergraphs and their applications in personalized medicine
报告人简介
Stefano Boccaletti is the Fellow of European Academy of Sciences, and the Fellow of the American Physical Society. He is currently Director of Research at the Institute of Complex Systems of the Italian CNR, in Florence. Boccaletti has published 352 papers in peer-reviewed international Journals, which received more than 45,000 citations (Google Scholar). His h factor is 78 and his i-10 index is 256.
内容简介
解析同一复杂系统不同个体样本间的差异，始终是核心研究难题，在生物学研究场景中尤为突出。旁侧网络能够检测个体关联模式相较于参考群体的偏离特征。本文提出旁侧超图通用分析框架，可识别任意相互作用阶数下高阶关联的异常变化。 我们先在合成数据集与基准数据集上完成方法有效性验证，随后将其应用于患者来源肿瘤类器官研究，精准捕捉疾病进展过程中正常组织与癌变组织之间基因表达的时序动态差异。该分析方法不仅能够复现已知的致癌特征标识，还挖掘出一个此前未被发现的潜在治疗靶点。 由于肿瘤类器官由每位患者单独构建，本研究框架首次搭建出一套基于高阶关联模式的个性化癌症治疗可行方案。该项研究成果以复杂系统理论为基础，为精准肿瘤学提供了全新的系统级研究策略。
