标题： 基于辅助信息的人类转录组概率分析 显示英文标题

标题： Probabilistic analysis of the human transcriptome with side information

摘要： 理解遗传信息的功能组织是现代生物学的一个主要挑战。 在2001年首次发表人类基因组序列之后，高通量测量技术的进步以及通过社区数据库高效共享研究材料，为研究生物体和生命结构提供了新的视角。 在本论文中，开发了新的计算策略来研究遗传信息的一个关键功能层——人类转录组，该转录组通过蛋白质合成调节活细胞的功能。 本论文的主要贡献是用于高通量数据分析的通用探索性工具，这些工具为细胞生物学网络、癌症机制和其他基因组功能方面提供了新的见解。 功能基因组学中的一个核心挑战是高维基因组观测结果与高水平的复杂且大部分未知的变异来源相关联。 通过结合多个测量源的统计证据以及基因组数据存储库中的丰富背景信息，已经能够解决与单个观测相关的某些不确定性，并识别出无法仅基于单个测量源检测到的功能机制。 统计学习和概率模型为这类建模任务提供了一个自然的框架。 关键方法论贡献的开源实现已被发布，以促进研究界进一步采用这些开发的方法。 显示英文摘要

摘要： Understanding functional organization of genetic information is a major challenge in modern biology. Following the initial publication of the human genome sequence in 2001, advances in high-throughput measurement technologies and efficient sharing of research material through community databases have opened up new views to the study of living organisms and the structure of life. In this thesis, novel computational strategies have been developed to investigate a key functional layer of genetic information, the human transcriptome, which regulates the function of living cells through protein synthesis. The key contributions of the thesis are general exploratory tools for high-throughput data analysis that have provided new insights to cell-biological networks, cancer mechanisms and other aspects of genome function. A central challenge in functional genomics is that high-dimensional genomic observations are associated with high levels of complex and largely unknown sources of variation. By combining statistical evidence across multiple measurement sources and the wealth of background information in genomic data repositories it has been possible to solve some the uncertainties associated with individual observations and to identify functional mechanisms that could not be detected based on individual measurement sources. Statistical learning and probabilistic models provide a natural framework for such modeling tasks. Open source implementations of the key methodological contributions have been released to facilitate further adoption of the developed methods by the research community.