人类转录组的边信息概率分析
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重大挑战。继2001年首次公布人类基因组序列之后，高通量测量技术的进步和通过社区数据库有效共享研究材料，为研究生物体和生命结构开辟了新的视角。在这篇论文中，新的计算策略已经被开发来研究遗传信息的一个关键功能层，人类转录组，它通过蛋白质合成来调节活细胞的功能。本论文的主要贡献是为高通量数据分析提供了通用的探索性工具，为细胞生物学网络，癌症机制和基因组功能的其他方面提供了新的见解。功能基因组学的一个核心挑战是，高维基因组观测与高水平的复杂和未知的变异来源有关。通过将多个测量源的统计证据与基因组数据库中丰富的背景信息相结合，可以解决与个体观察相关的一些不确定性，并识别基于个体测量源无法检测到的功能机制。统计学习和概率模型为这种建模任务提供了一个自然的框架。关键方法学贡献的开源实现已经发布，以促进研究界进一步采用所开发的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
