Bayesian Methods for High-dimensional and Correlated Data

高维和相关数据的贝叶斯方法

基本信息

  • 批准号:
    RGPIN-2014-05010
  • 负责人:
  • 金额:
    $ 1.02万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2017
  • 资助国家:
    加拿大
  • 起止时间:
    2017-01-01 至 2018-12-31
  • 项目状态:
    已结题

项目摘要

The accelerated development of many high-throughput biotechnologies has made it affordable to collect measurements of high-dimensional molecular changes in cells, such as expressions of genes, which are called features generally, and often called signatures in the literature of life sciences. Scientists are interested in discovering relevant features associated with a categorical response variable, such as cancer onset or progression. The numbers of such candidate features at our disposal are often as large as millions. The high dimension in candidate features presents great challenges to statisticians because we are much more likely to find noise rather than signals. In addition, these features have complex structures. I will work to develop and apply Bayesian statistical methodologies based on MCMC (Markov chain Monte Carlo) computing and heavy-tailed priors to identify relevant feature subsets associated with a response of interest, and to other research problems in life sciences that use high-throughput data. The outcomes from this research will include new statistical software packages (with new algorithms) for solving bioinformatics and neuroinformatics problems. These software packages are expected to facilitate new scientific discoveries, which will potentially lead to advances in diagnosis and prognosis of many complex human diseases, particularly cancers. I also propose to extend existing model evaluation methods so that they are applicable to complex Bayesian models for correlated data, such as spatial and temporal data, which often arise from epidemiological, ecological, and environmental studies. I expect that the extended model evaluation methods will be more reliable tools for evaluating models for correlated data. Better model evaluation results will not only help us choose the best model, but also guide us to improve existing models and then develop better models. Therefore, the extended model evaluation methods for correlated data will assist investigators working in epidemiological, ecological, and environmental studies to develop or choose appropriate models for their data sets, and then draw reliable conclusions and make better predictions based on their model fitting results.
许多高通量生物技术的加速发展使得收集细胞中的高维分子变化的测量变得负担得起,例如基因的表达,其通常被称为特征,并且在生命科学文献中通常被称为签名。科学家们有兴趣发现与分类反应变量相关的相关特征,如癌症发作或进展。可供我们使用的候选特征的数量通常高达数百万。候选特征中的高维数给统计学家带来了巨大的挑战,因为我们更有可能找到噪声而不是信号。此外,这些特征具有复杂的结构。我将致力于开发和应用基于MCMC(马尔可夫链蒙特卡罗)计算和重尾先验的贝叶斯统计方法,以识别与感兴趣的响应相关的相关特征子集,以及使用高通量数据的生命科学中的其他研究问题。这项研究的成果将包括用于解决生物信息学和神经信息学问题的新统计软件包(具有新算法)。预计这些软件包将促进新的科学发现,这将有可能导致许多复杂的人类疾病,特别是癌症的诊断和预后的进步。我还建议扩展现有的模型评估方法,使它们适用于复杂的贝叶斯模型的相关数据,如空间和时间的数据,这往往是从流行病学,生态和环境研究。我希望扩展的模型评估方法将是评估相关数据模型的更可靠的工具。更好的模型评估结果不仅可以帮助我们选择最好的模型,还可以指导我们改进现有模型,然后开发更好的模型。因此,相关数据的扩展模型评估方法将有助于流行病学、生态学和环境研究人员为他们的数据集开发或选择合适的模型,然后根据模型拟合结果得出可靠的结论并做出更好的预测。

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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Li, Longhai其他文献

Study on Erosion Behavior of Laser Wire Feeding Cladding High-Manganese Steel Coatings.
  • DOI:
    10.3390/ma16175733
  • 发表时间:
    2023-08-22
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Guo, Huafeng;Zhang, Chenglin;He, Yibo;Yang, Haifeng;Zhao, Enlan;Li, Longhai;He, Shaohua;Liu, Lei
  • 通讯作者:
    Liu, Lei
Serum Chemokine CXCL7 as a Diagnostic Biomarker for Colorectal Cancer
  • DOI:
    10.3389/fonc.2019.00921
  • 发表时间:
    2019-10-09
  • 期刊:
  • 影响因子:
    4.7
  • 作者:
    Li, Longhai;Zhang, Lihua;Mao, Yong
  • 通讯作者:
    Mao, Yong
Bias-Corrected Hierarchical Bayesian Classification With a Selected Subset of High-Dimensional Features
Morphology and nanoindentation properties of mouthparts in Cyrtotrachelus longimanus (Coleoptera: curculionidae)
Cyrtotrachelus longimanus(鞘翅目:象甲科)口器的形态和纳米压痕特性
  • DOI:
    10.1002/jemt.22855
  • 发表时间:
    2017-07-01
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    Li, Longhai;Guo, Ce;Han, Cheng
  • 通讯作者:
    Han, Cheng
The formation mechanism of titania nanotube arrays in hydrofluoric acid electrolyte
氢氟酸电解液中二氧化钛纳米管阵列的形成机理
  • DOI:
    10.1007/s10853-007-2418-8
  • 发表时间:
    2008-01
  • 期刊:
  • 影响因子:
    4.5
  • 作者:
    Zou, Lexi;Zheng, Qing;Shao, Jiahui;Liao, Junsheng;Bai, Jing;Li, Longhai;Cai, Weimin;Zhou, Baoxue;Liu, Yanbiao;Zhu, Xinyuan
  • 通讯作者:
    Zhu, Xinyuan

Li, Longhai的其他文献

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{{ truncateString('Li, Longhai', 18)}}的其他基金

Predictive Methods for Analyzing High-throughput Data and Spatial-Temporal Data
分析高通量数据和时空数据的预测方法
  • 批准号:
    RGPIN-2019-07020
  • 财政年份:
    2022
  • 资助金额:
    $ 1.02万
  • 项目类别:
    Discovery Grants Program - Individual
Predictive Methods for Analyzing High-throughput Data and Spatial-Temporal Data
分析高通量数据和时空数据的预测方法
  • 批准号:
    RGPIN-2019-07020
  • 财政年份:
    2021
  • 资助金额:
    $ 1.02万
  • 项目类别:
    Discovery Grants Program - Individual
Predictive Methods for Analyzing High-throughput Data and Spatial-Temporal Data
分析高通量数据和时空数据的预测方法
  • 批准号:
    RGPIN-2019-07020
  • 财政年份:
    2020
  • 资助金额:
    $ 1.02万
  • 项目类别:
    Discovery Grants Program - Individual
Predictive Methods for Analyzing High-throughput Data and Spatial-Temporal Data
分析高通量数据和时空数据的预测方法
  • 批准号:
    RGPIN-2019-07020
  • 财政年份:
    2019
  • 资助金额:
    $ 1.02万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian Methods for High-dimensional and Correlated Data
高维和相关数据的贝叶斯方法
  • 批准号:
    RGPIN-2014-05010
  • 财政年份:
    2018
  • 资助金额:
    $ 1.02万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian Methods for High-dimensional and Correlated Data
高维和相关数据的贝叶斯方法
  • 批准号:
    RGPIN-2014-05010
  • 财政年份:
    2016
  • 资助金额:
    $ 1.02万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian Methods for High-dimensional and Correlated Data
高维和相关数据的贝叶斯方法
  • 批准号:
    RGPIN-2014-05010
  • 财政年份:
    2015
  • 资助金额:
    $ 1.02万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian Methods for High-dimensional and Correlated Data
高维和相关数据的贝叶斯方法
  • 批准号:
    RGPIN-2014-05010
  • 财政年份:
    2014
  • 资助金额:
    $ 1.02万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient Bayesian analysis for complex models
复杂模型的高效贝叶斯分析
  • 批准号:
    356014-2009
  • 财政年份:
    2013
  • 资助金额:
    $ 1.02万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient Bayesian analysis for complex models
复杂模型的高效贝叶斯分析
  • 批准号:
    356014-2009
  • 财政年份:
    2012
  • 资助金额:
    $ 1.02万
  • 项目类别:
    Discovery Grants Program - Individual

相似国自然基金

Computational Methods for Analyzing Toponome Data
  • 批准号:
    60601030
  • 批准年份:
    2006
  • 资助金额:
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癌症研究中复杂、高维功能数据的贝叶斯方法
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