报告题目：Parenclitic hypergraphs and their applications in personalized medicine
报告时间：2026年5月21日上午9:00
报告地点：物理楼226报告厅
报告人：Stefano Boccaletti 教授
邀请人：周杰 副教授
报告人单位：The Institute of Complex Systems of the ltalian
报告人简介：
Stefano Boccalettiis 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.
报告摘要：
Understanding the differences between individual instances of the same complex system remains a central challenge, particularly in biological contexts. Parenclitic networks offer a means to detect deviations in correlations with respect to reference populations. Here, we introduce parenclitic hypergraphs, general framework for identifying anomalies in higher-order correlations across arbitrary interaction orders. After validating the method on synthetic datasets and benchmark ones, we apply it to patient-derived cancer organoids, capturing temporal changes in gene expression between healthy and cancerous tissues as the disease progresses. Our approach not only reproduces known oncogenic signatures, but also reveals a previously unrecognized candidate therapeutic target. Since organoids are generated from individual patients, our framework provides, for the first time, a viable protocol for personalized cancer therapy based on higher-order correlation patterns. These findings offer a novel, systems-level strategy for precision oncology grounded in complex systems theory.