Statistical methods for integromics discoveries
整合组学发现的统计方法
基本信息
- 批准号:7740132
- 负责人:
- 金额:$ 31.8万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2009
- 资助国家:美国
- 起止时间:2009-09-01 至 2010-08-31
- 项目状态:已结题
- 来源:
- 关键词:Acquired Immunodeficiency SyndromeAddressBinding SitesBiologicalBiomedical ResearchBiotechnologyCellsCollectionComplexComputing MethodologiesDataDiseaseEnvironmentEtiologyGene ExpressionGene ProteinsGenesGenomicsGoalsHuman bodyIndividualInformation SystemsInformation TheoryLeadLearningMalignant NeoplasmsMethodologyMethodsModelingOrganPhysiologyPlayProcessRecoveryRoleSignal TransductionSignal Transduction PathwaySignaling MoleculeStatistical MethodsStatistical ModelsSystemSystems BiologyTechniquesTestingYeastsbasebiological systemscomputerized toolsepigenomicsgenome sequencinghigh throughput technologyhuman diseaseinsightmetabolomicsmodel designnoveltooltranscription factortranscriptomics
项目摘要
DESCRIPTION (provided by applicant):
Contemporary systems biology is shifting the paradigm of biomedical research from minimalistic studies of individual genes/proteins to integration of information at systems level. Current high throughput biotechnologies enable collection of a large amount of biological information, and the different aspects of the cellular systems are reflected with heterogeneous data, e.g., genomics, epigenomics, transcriptomics and metabolomics. However, it remains a major challenge to systematically integrate this body of information and derive biological insights at a mechanistic level. The overarching goal of this project is to develop a computational system that enables integration of various high throughput "omics" data (an "integromics" approach) to gain insights into cellular systems, in particular the signal transduction systems. The activities of the project are organized into four specific aims, which progress from approaches for capturing general information among the multiple omics data to more specific and complex models designed to decipher specific cellular signaling systems. Firstly, we will develop a general framework, based on information theory and probabilistic models, to identify information modules that convey biological information between different "omics" data at large scale. Secondly, we develop methods to further investigate if the information from the multiple omics data reflects causal relationships. Thirdly, we will develop tools to recover missing information from the system to augment the high throughput technologies. Finally, we will develop a unified model to elucidate signal transduction pathways by integrating information form multiple omics data in manner that is both biologically sensible and mathematically rigorous. We expect that the methodologies developed in the project are widely applicable to study a variety of cellular signal transduction systems.
描述(由申请人提供):
当代系统生物学正在改变生物医学研究的范式,从个体基因/蛋白质的最低限度的研究到系统水平的信息整合。当前的高通量生物技术使得能够收集大量的生物信息,并且细胞系统的不同方面用异质数据反映,例如,基因组学、表观基因组学、转录组学和代谢组学。然而,它仍然是一个重大的挑战,系统地整合这一机构的信息,并在机械水平上获得生物学的见解。该项目的总体目标是开发一个计算系统,该系统能够整合各种高通量“组学”数据(一种“智能”方法),以深入了解细胞系统,特别是信号转导系统。该项目的活动分为四个具体目标,从捕获多组学数据中的一般信息的方法到旨在破译特定细胞信号系统的更具体和复杂的模型。首先,我们将开发一个通用的框架,基于信息理论和概率模型,以识别信息模块,在不同的“组学”数据之间传递生物信息。其次,我们开发的方法,以进一步调查,如果从多个组学数据的信息反映因果关系。第三,我们将开发工具来恢复系统中丢失的信息,以增强高吞吐量技术。最后,我们将开发一个统一的模型来阐明信号转导途径,通过整合信息形成多个组学数据的方式,这是生物学上明智的和数学上严格的。我们希望在该项目中开发的方法是广泛适用于研究各种细胞信号转导系统。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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XINGHUA LU其他文献
XINGHUA LU的其他文献
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{{ truncateString('XINGHUA LU', 18)}}的其他基金
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用于转化医学的可解释深度学习模型
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Interpretable deep learning models for translational medicine
用于转化医学的可解释深度学习模型
- 批准号:
10371139 - 财政年份:2015
- 资助金额:
$ 31.8万 - 项目类别:
Interpretable deep learning models for translational medicine
用于转化医学的可解释深度学习模型
- 批准号:
10171908 - 财政年份:2015
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Deciphering cellular signaling system by deep mining a comprehensive genomic compendium
通过深入挖掘全面的基因组纲要来破译细胞信号系统
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9042426 - 财政年份:2015
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$ 31.8万 - 项目类别:
Ontology-Driven Methods for Knowledge Acquisition and Knowledge Discovery
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8714053 - 财政年份:2011
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