Statistical Problems in Hidden Markov Modeling for Biology and Chemistry

提案ID：DMS-0204674PI：Jun S.LiuTitle：用于生物和化学的隐马尔可夫模型中的统计问题摘要随着许多物种基因组的完成和微阵列技术的进步，生物研究人员开始拥有大量的数据-但这些“原始产品”仍远未可用。本世纪最具挑战性的问题之一是破译这些海量的生物信息，将数据转化为知识。与此同时，化学研究也发生了一场革命：科学家现在可以使用先进的技术来观察单分子动力学，这有望改写从传统的系综平均实验中衍生出来的一些物理学和化学的基本定律。由于有关分子运动的数据本身就有噪音，因此迫切需要开发先进的统计工具来处理这类数据。过去的十年见证了形式统计建模的力量，特别是隐马尔可夫模型的使用，在计算生物学领域发生了革命性的变化。研究人员认为，使用适当的统计模型来描述潜在的化学过程并得出有效的推断方法，也可以极大地加强单分子研究中的数据分析。对于生物信息分析，研究人员描述了与用于寻找基序的统计模型相关的几个问题，这些问题的解决可以加深对几种流行的用于序列分析的生物信息学算法的理解。研究人员表明，这些算法是基于特殊的隐马尔可夫或半马尔可夫模型，可以进行推广，以适应更详细的生物学知识。对于单分子数据分析，研究人员概述了一种有效的基于似然的方法，用于推断单分子研究中的大量特殊兴趣。观测数据的形式自然需要数据增强框架，这是一种很有前途的解决计算困难的方法。在单分子研究中，除了模型推断的问题外，模型的选择也是一项重要而困难的任务，因为经常会有描述一个化学反应的相互竞争的模型，这就需要使用实验数据来选择合适的模型。研究人员使用数据增强方法，提出了几种在不同化学模型中进行选择的通用方法。

Proposal ID: DMS-0204674PI: Jun S. LiuTitle: Statistical problems in hidden Markov modeling for biology and chemistryAbstractWith the completion of genomes of many species and the advances of microarray technologies, biological researchers begin to possess a tremendous amount of data --- but these "raw products" are still far from usable. One of the most challenging problems of this century is to decipher this huge amount of biological information, turning the data into knowledge. Simultaneously, there also has been a revolution in chemistry research: scientists can now use advanced technology to make observations on single-molecule dynamics, which promises to rewrite some fundamental laws in physics and chemistry derived from traditional ensemble-averaged experiments. As the data concerning molecular movements are inherently noisy, the development of advanced statistical tools for handling such data is a pressing need. The past decade has witnessed the power of formal statistical modeling, especially the use of hidden Markov models, in revolutionizing the field of computational biology. It is the investigators' belief that using proper statistical models to describe the underlying chemical processes and to derive efficient inference methods can also greatly strengthen the data analysis in single-molecule studies. For the biological information analysis, the investigators describe a few problems related to the statistical models used for finding motifs, whose solutions can deepen the understanding of a few popular Bioinformatics algorithms for sequence analysis. The investigators show that these algorithms are based on special hidden Markov or semi-Markov models and can be generalized to accommodate more detailed biology knowledge. For single-molecule data analysis, the investigators outline an efficient likelihood-based approach for inferring quantities of special interests in single-molecule studies. The form of the observed data naturally calls for a data augmentation framework, which is a promising means for solving the computational difficulty. In single-molecule studies, besides the problem of model inference, model selection is also an important and difficult task, as it is often the case that there are competing models describing one chemical reaction, making it necessary to use the experimental data to choose the appropriate model. The investigators, using a data augmentation approach, propose a few generalized methods for choosing among different chemical models.