北京雁栖湖应用数学研究院

高瑞泽 , 韩立岩 , 李振 , 龙飞 , 史冬波 , 汤珂 , 张琦

董昂

2025年10月17日 15:00 至 16:00

A3-2-303

Zoom 435 529 7909 (BIMSA)

Networks are fundamental to understanding complex systems, characterized by many underlying entities and their intricate interactions. We contextualize evolutionary game theory and community ecology theory to explain how the dynamic change of an entity is determined by its own strategy and the strategies of its interacting counterparts. We derive a system of nonlinear mixed ordinary differential equations (nMODEs) to quantify the contributions of these two types of strategies and encode them into informative, dynamic, omnidirectional, and personalized networks (idopNetworks). We implement multi-task learning (MTL) into the matrix representation of linearized nMODEs to jointly choose a subset of the most significant entities (acting as predictors) for all entities each viewed as a response. In going beyond existing networking practice, idopNetworks can capture all-around interacting links, nonlinearities, and emergent properties of a complex system, which, to a larger extent, approximate the intricate and multifaceted nature of complex systems. We apply our model to learn gene regulatory idopNetworks from transcriptional data, identifying previously-unknown regulatory roles of several genes in mediating malaria infection. We perform computer simulation to validate the statistical relevance of the model. Our model provides a new insight of machine learning to analyze, model, and interpret complex data in a non-Euclidean space.

北京雁栖湖应用数学研究院助理研究员，研究方向为统计建模与复杂生物系统机理解析，主要从事复杂系统多尺度调控网络的构建与分析。2023年获北京林业大学计算生物学博士学位，随后在北京雁栖湖应用数学研究院从事博士后研究工作。已在 PNAS、Nature Communications、Methods in Ecology and Evolution 等重要期刊发表论文20余篇，其中第一作者SCI论文4篇、共同第一作者2篇、通讯作者1篇。