长链非编码RNA（lncRNA）与蛋白质的协同关系在人类多种重大疾病中具有重要作用。针对协同过程中涉及的多种分子关系以及动态性特点，本项目将研究面向疾病的lncRNA-蛋白质异构时序网络构建及模块分析方法。首先，通过引入分子结构信息并结合深度学习方法，建立lncRNA-蛋白质相互作用预测模型；然后，提出基于疾病本体术语继承与扩展语义的lncRNA功能相似性度量方法，进而构建lncRNA-lncRNA功能关联网络；随后，以lncRNA-lncRNA、lncRNA-蛋白质以及蛋白质-蛋白质三种分子关系为基础，通过融合不同时段的基因表达谱数据建立lncRNA-蛋白质异构时序网络；最后，研究适合于该网络的功能模块识别与时序演化分析方法。本项目对于发展信息科学与生物医学交叉的生物网络构建与分析方法具有重要的理论研究意义，同时在探索lncRNA调控人类疾病的分子机制方面具有潜在的应用价值。

The synergism of long non-coding RNA (lncRNA) and protein plays a crucial role in a variety of major human diseases. Aiming at several molecular relationships and dynamic characteristic in the synergism, this project will study the construction of lncRNA-protein heterogeneous temporal network and module analysis method in the network. Firstly, a method of predicting lncRNA-protein interaction will be proposed by introducing the information of molecular structure and using deep learning method. Secondly, a novel method of measuring lncRNA functional similarity based on inheritance and extension semantics of terms in disease ontology will be proposed, and a lncRNA-lncRNA functional association network will be further constructed. Thirdly, based on three kinds of molecular relationships including lncRNA-lncRNA, lncRNA-protein and protein-protein, a lncRNA-protein heterogeneous temporal network will be constructed by integrating time-course gene expression data. Finally, we will study identification and temporal evolution analysis methods of lncRNA-protein function module suitable for the constructed network. This project will be of great theoretical significance for the development of construction and analysis methods of biological network as an interdisciplinary problem of information science and biomedical fields, and has potential application values on exploring the molecular mechanism of lncRNA in the regulation of human diseases.