Managing the Data Explosion in Post-Genomic Biology with Fast Bayesian Computational Methods

使用快速贝叶斯计算方法管理后基因组生物学中的数据爆炸

• 批准号: EP/F027400/1
• 负责人: David Wild
• 金额: $ 33.63万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF027400%2F1
• 关键词: Managing; Data; Explosion; Post; Genomic

Rapid technological advances in molecular biology are providing an unprecedented opportunity to investigate the basic processes of life. This `post-genomic' phase of molecular biology has resulted in an explosion of typically high dimensional structured data from new technologies for transcriptomics (microarrays), proteomics and metabolomics. Such data requires novel mathematical, statistical and computational methods for their interpretation and analysis. This proposal focuses on the development of statistical and computational methods for the analysis of such data, using novel approaches from the fields of machine learning and nonparametric Bayesian statistics. The project involves a close collaboration of scientists with expertise in machine learning and statistics, bioinformatics and molecular biology. The new software tools will be developed in the context of real-world scientific problems, such as: elucidating signalling networks in plant stress responses; metabolic regulation in the bacteria Streptomyces, major producers of antibiotics and delineating the molecular mechanisms contributing to mitochondrial dysfunction in obesity and diabetes. The scientific goal of the project will be to apply these novel methods to modelling bioinformatics data, but the methods developed will be broadly applicable across a number of fields.

分子生物学技术的快速发展为研究生命的基本过程提供了前所未有的机会。分子生物学的这一“后基因组”阶段导致了来自转录组学（微阵列）、蛋白质组学和代谢组学新技术的典型高维结构化数据的爆炸式增长。这些数据需要新的数学、统计和计算方法来解释和分析。该提案的重点是开发用于分析此类数据的统计和计算方法，使用机器学习和非参数贝叶斯统计领域的新方法。该项目涉及具有机器学习和统计学、生物信息学和分子生物学专业知识的科学家的密切合作。新的软件工具将在现实世界的科学问题的背景下开发，例如：阐明植物应激反应中的信号网络;抗生素的主要生产者链霉菌的代谢调节，以及描绘导致肥胖和糖尿病中线粒体功能障碍的分子机制。该项目的科学目标是将这些新方法应用于生物信息学数据建模，但开发的方法将广泛适用于许多领域。

项目成果

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.

• DOI: 10.1371/journal.pone.0059795
• 发表时间: 2013
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Darkins R;Cooke EJ;Ghahramani Z;Kirk PD;Wild DL;Savage RS
• 通讯作者: Savage RS

NONPARAMETRIC BAYESIAN SPARSE FACTOR MODELS WITH APPLICATION TO GENE EXPRESSION MODELING

• DOI: 10.1214/10-aoas435
• 发表时间: 2011-06-01
• 期刊: ANNALS OF APPLIED STATISTICS
• 影响因子: 1.8
• 作者: Knowles, David;Ghahramani, Zoubin
• 通讯作者: Ghahramani, Zoubin

R/BHC: fast Bayesian hierarchical clustering for microarray data.

• DOI: 10.1186/1471-2105-10-242
• 发表时间: 2009-08-06
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Savage RS;Heller K;Xu Y;Ghahramani Z;Truman WM;Grant M;Denby KJ;Wild DL
• 通讯作者: Wild DL

Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements.

• DOI: 10.1186/1471-2105-12-399
• 发表时间: 2011-10-13
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Cooke EJ;Savage RS;Kirk PD;Darkins R;Wild DL
• 通讯作者: Wild DL

Discovering transcriptional modules by Bayesian data integration.

• DOI: 10.1093/bioinformatics/btq210
• 发表时间: 2010-06-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Savage RS;Ghahramani Z;Griffin JE;de la Cruz BJ;Wild DL
• 通讯作者: Wild DL