ADVANCED MCMC ALGORITHMS FOR BIOMEDICAL DATA ANALYSIS

描述(改编自申请者的摘要)：大而迅速 不断增长的序列和结构数据库为 生物医学科学。计算方法的有用性提取 来自这些数据库的信息，以解决一些最困难和 分子和结构生物学中的重要问题日益突出 很明显。但是，这些数据通常包含以下几个特征 众所周知，这会使它们对分析产生抵触，包括对 丢失数据，存在似然性或具有多个 局部极值，或需要控制用于 描述这些复杂的数据。在其中一些问题上已经取得了进展， 最值得注意的是，通过使用贝叶斯递归来解决数据丢失问题 算法、期望最大化算法和隐马尔可夫模型 (HMM)和MCMC采样算法。然而，其他问题在很大程度上仍然存在 悬而未决。MCMC技术的最新进展开辟了新的途径 对于这些棘手的数据分析问题。具体地说，最近出现的 多尺度MCMC算法中最优解的选择 粗糙地貌和可逆跳跃MCMC算法的开发 对问题维度的推断引发了这方面的变化 竞技场。在过去的几个月里，一类多阶段的MCMC算法，称为 模拟烧结，允许对粗糙的地形进行贝叶斯推理 包括许多可逆跳转算法中固有的那些，提出了一种 为这些非常困难的数据分析提供突破的机会 挑战。这项研究的目的是探索发展， 适应和应用这些方法，以应对一些重大挑战 分子和结构生物学。

DESCRIPTION (Adapted from the applicant's abstract): Large and rapidly growing sequence and structural databases provide a vast new resource for the biomedical sciences. The usefulness of computational approaches to extract information from these databases to address some of the most difficult and important problems in molecular and structural biology has become increasingly apparent. However, these data often contain several characteristics that are well known to render them resistant to analysis, including presentations of missing data, the existence of likelihood or posterior surfaces with multiple local extremes, or the need to control the dimensional size of models used to describe these complex data. Progress has been made on some of these issues, most notably the missing data problem, through the use of Bayesian recursive algorithms, expectations maximization algorithms, and hidden Markov models (HMM) and MCMC sampling algorithms. However, the other issues remain largely unsolved. Recent advances in MCMC technology have opened up fresh approaches to these difficult data analysis problems. Specifically, the recent emergence of multi-scales MCMC algorithms which are effective in identifying optima in rough landscapes, and the development of reversible jump MCMC algorithms for inferences on the dimension of a problem, have initiated changes in this arena. In the last few months, a class of multistage MCMC algorithms, called simulated sintering, which permit Bayesian inference on rough landscapes including those inherent in many reversible jumping algorithms, present an opportunity for a breakthrough for these very difficult data analysis challenges. The aims of this research are to explore the development, adaptation, and application of these methods to some of the grand challenges of molecular and structural biology.