基于图与组合优化的生物数据和网络数据挖掘算法研究

生物技术的飞速发展产生了海量的生物数据，并且正在和继续成爆炸式增长。虽然计算机技术的发展为大数据科学研究提供了强大的计算能力和计算资源，已使得用计算机处理分析生物大数据并进而研究生命科学成为现实,但是相比生物大数据的不断积累速度对不同类型大数据的整合分析却严重滞后，使得数据的价值难以充分发挥。究其原因是缺乏高效可靠的生物大数据挖掘算法和软件，从而给数学和计算机科学带来了无限的机遇和挑战。本项目旨在用组合最优化的理论和方法研究生物医学大数据科学领域中的重大科学问题，研究内容属于生物信息学的范畴，具有鲜明的学科交叉特征。针对生物信息学领域中的重大科学问题，譬如：组学数据组装、基因表达数据分析、组合药物筛选、功能脑网络构建等，研发高效可靠的算法。在理论上实现分科知识融通并形成完整的知识体系，在技术上突破计算瓶颈并使其算法软件具有高度的普适性，在应用上使其理论和产品在生物医学领域得到广泛的认可。

The rapid development of biotechnology has produced massive amounts of biological data and is continuing and exploding. Although the development of computer technology provides powerful computing resources, and enables the analysis of biological big data and the study of life sciences, the integration analysis of big data is seriously lagging behind, making the value of data difficult to fully play. The reason is the lack of efficient and reliable bio-big data mining algorithms and software, which therefore brings unlimited opportunities and challenges to mathematicians and computer scientists. This project aims to study major scientific issues in the field of biomedical big data science by using combinatorial optimization theories and methods. The research content belongs to the category of bioinformatics and has distinct interdisciplinary characteristics. For the major scientific issues in the field of bioinformatics, such as: genomic data assembly, gene expression data analysis, drug combination screening, functional brain network construction, etc., we develop efficient and reliable algorithms. In theory, a complete knowledge system will be formed by fusion of disciplinaries. In technology, we will break technical bottleneck and develop highly universal software. In practice, the new theories and products will be widely recognized in the biomedical field.