ADVANCED MCMC ALGORITHMS FOR BIOMEDICAL DATA ANALYSIS

用于生物医学数据分析的先进 MCMC 算法

• 批准号: 2829243
• 负责人: Charles E Lawrence
• 金额: $ 9.24万
• 依托单位: WADSWORTH CENTER
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-05-01 至 2001-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2829243
• 关键词: bioengineering /biomedical engineering; computer assisted sequence analysis; computer simulation; computer system design /evaluation; informatics; molecular biology information system; statistics /biometry; structural biology

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.

描述（改编自申请人摘要）：大型且快速 不断增长的序列和结构数据库为生物学提供了巨大的新资源。 生物医学科学 计算方法提取的有用性 这些数据库中的信息，以解决一些最困难的， 分子和结构生物学中的重要问题已经变得越来越多 很明显 然而，这些数据通常包含几个特征， 众所周知，使他们耐分析，包括介绍， 缺失数据，存在可能性或多个后表面 局部极值，或需要控制用于 描述这些复杂的数据。 其中一些问题已经取得进展， 最值得注意的是缺失数据问题，通过使用贝叶斯递归 算法、期望最大化算法和隐马尔可夫模型 (HMM)MCMC采样算法。 然而，其他问题在很大程度上仍然存在 未破案件 MCMC技术的最新进展开辟了新的方法 解决这些数据分析难题。 具体来说，最近出现的 的多尺度MCMC算法，有效地识别最优解， 粗糙的景观，和可逆跳MCMC算法的发展， 对问题的维度的推断，已经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MCMC算法，称为 模拟烧结，允许对粗糙地形进行贝叶斯推断 包括许多可逆跳跃算法中固有的那些，提出了一种 这些非常困难的数据分析的突破机会 挑战 本研究的目的是探讨发展， 这些方法的适应和应用， 分子和结构生物学。