基于动态网络的复杂疾病分析理论和方法

Complex diseases such as cancer and diabetes have seriously influenced human health. Dissecting occurrence and development processes of complex diseases is a great challenge in the biological and medical fields, and is crying for developing new theories and new methods. Based on biological experimental data and clinical diagnosis data, it is of important significance to develop computable modeling theories and highly efficient algorithms as well as to uncover the mechanisms of how human major diseases are generated from the viewpoint of dynamical networks. This integrating project carries out the promoting study on the pathogenic mechanisms of complex diseases on the basis of previous studies through integrating resource, uniting power and concentrating superiority. The main studies focus on theories and methods of predicting new data based on a short sequence of high-dimensional data, the methods of constructing dynamical networks for complex diseases, the methods of identifying dynamical network biomarks, and the methods of detecting the causal relationship between pathogenic factors. By this series of studies, the project tries to establish a new set of theories and methods, which are not only used to reveal the pathogenic mechanisms of complex diseases but applied to earlier prediction, diagnosis, treatment and control of some major complex diseases. Theories and methods to be established in this project would lay a solid foundation and provide methodology for revealing complex life phenomena, exploring pathogenic mechanisms and identifying early warning signals of complex diseases based on a short sequence of high-dimensional data.

复杂疾病如癌症、糖尿病等已严重影响到人类健康，剖析这种疾病的发生和发展过程是当前生物医学领域面临的重大挑战，迫切需要发展新理论和方法。基于生物实验数据和临床医学数据，发展可计算建模理论和高效算法，在动态网络的层次上解析人类重大疾病的发病机理具有重要意义。本集成项目在动态网络的前期研究基础上，通过整合资源、凝聚力量、集中优势，开展重大复杂疾病致病机理的提升研究。主要研究内容包括：基于短时间序列的高维数据预测新数据的理论与方法、复杂疾病动态网络的构建方法、动态网络标记物的识别方法、致病因子间因果关系的检测方法等。通过本集成项目的研究，试图发展一套全新且实用的理论与方法，以便解析复杂疾病的致病机理，并能应用于重大复杂疾病的早期预测、诊断、治疗和控制。本集成项目所形成的理论和方法成果将为基于短时间序列的高维数据解析复杂的生命现象、探索复杂疾病的发病机理和复杂疾病的早期诊断奠定理论基础并提供方法论。

复杂疾病如癌症、糖尿病等已严重影响到人类健康，剖析这种疾病的发生和发展过程是当前生物医学领域面临的重大挑战，迫切需要发展新理论和方法。基于生物实验数据和临床医学数据，发展可计算建模理论和高效算法，在动态网络的层次上解析人类重大疾病的发病机理具有重要意义。本集成项目在动态网络的前期研究基础上，通过整合资源、凝聚力量、集中优势，开展重大复杂疾病致病机理的提升研究。主要研究内容包括：基于短时间序列的高维数据预测新数据的理论与方法、复杂疾病动态网络的构建方法、动态网络标记物的识别方法、致病因子间因果关系的检测方法等。通过本集成项目的研究，试图发展一套全新且实用的理论与方法，以便解析复杂疾病的致病机理，并能应用于重大复杂疾病的早期预测、诊断、治疗和控制。本集成项目所形成的理论和方法成果将为基于短时间序列的高维数据解析复杂的生命现象、探索复杂疾病的发病机理和复杂疾病的早期诊断奠定理论基础并提供方法论。