CLUSTERING BIOLOGICAL DATA USING MESSAGE PASSING

使用消息传递对生物数据进行聚类

• 批准号: 7960264
• 负责人: HESHAM ALI
• 金额: $ 3.83万
• 依托单位: UNIVERSITY OF NEBRASKA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7960264
• 关键词: Accounting; Algorithms; Bioinformatics; Biological; Classification; Computer Retrieval of Information on Scientific Projects Database; Data; Data Set; Funding; Gene Expression; Genes; Genus Mycobacterium; Grant; Institution; Methods; Nebraska; Performance; Phylogenetic Analysis; Process; Research; Research Personnel; Resources; Simulate; Source; Structure; Trees; United States National Institutes of Health; functional genomics; improved

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Clustering algorithms are widely used in bioinformatics to classify data, as in the analysis of gene expression and in the building of phylogenetic trees. Biological data often describe parallel and spontaneous processes. To capture these features, we propose a new clustering algorithm that employs the concept of message passing. Message Passing Clustering (MPC) allows data objects to communicate with each other and produces clusters in parallel, thereby making the clustering process intrinsic. We have proved that MPC shares similarity with Hierarchical Clustering (HC) but offers significantly improved performance because it takes into account both local and global structure. We have analyzed 35 sets of simulated dynamic gene expression data, achieving a 95% hit rate in which 639 genes out of a total 674 genes were correctly clustered. We have also applied MPC to a real data set to build a phylogenic tree from aligned mycobacterium sequences. The results show higher classification accuracies as compared to traditional clustering methods such as HC.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 聚类算法广泛用于生物信息学中对数据进行分类，例如基因表达分析和系统发育树的构建。生物数据通常描述并行和自发的过程。为了捕捉这些特征，我们提出了一种采用消息传递概念的新聚类算法。消息传递集群 (MPC) 允许数据对象相互通信并并行生成集群，从而使集群过程成为内在的。 我们已经证明 MPC 与层次聚类 (HC) 具有相似性，但由于它同时考虑了局部和全局结构，因此性能显着提高。 我们分析了 35 组模拟动态基因表达数据，达到了 95% 的命中率，总共 674 个基因中有 639 个基因被正确聚类。 我们还将 MPC 应用于真实数据集，以根据比对的分枝杆菌序列构建系统发育树。 结果显示，与 HC 等传统聚类方法相比，分类精度更高。