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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

基本信息

批准号：
EP/F028504/1
负责人：
Jim Griffin
金额：
$ 1.46万
依托单位：
University of Kent
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%2FF028504%2F1
关键词：
Managing Data Explosion Post Genomic

项目摘要

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.

分子生物学的快速技术进步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“后基因组”阶段导致了转录组学（微阵列）、蛋白质组学和代谢组学新技术中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。本提案的重点是统计和计算方法的发展，用于分析这些数据，使用来自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科学问题的背景下开发，例如：阐明植物胁迫反应中的信号网络；细菌链霉菌的代谢调节，抗生素的主要生产者，并描绘导致肥胖和糖尿病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，但所开发的方法将广泛适用于许多领域。