III: Medium: Meta-analysis reinterpreted using causal graphs

III:中:使用因果图重新解释荟萃分析

基本信息

  • 批准号:
    1302448
  • 负责人:
  • 金额:
    $ 112.08万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2013
  • 资助国家:
    美国
  • 起止时间:
    2013-07-15 至 2019-06-30
  • 项目状态:
    已结题

项目摘要

Statistical conclusions from research studies may often be misleading due to a variety of reasons including small sample sizes for the studies or confounding factors which are unknown to the investigators of the study. One way to reduce the possibility of misleading conclusions is to combine the results of multiple research studies using a technique referred to as "meta-analysis." Meta-analysis is one of the most widely used techniques to infer knowledge from data in science. The idea behind meta-analysis studies is that the combined statistical conclusions from multiple research studies reflect the information in all of the studies and are more likely to be accurate. The conclusions from meta-analyses are considered "better" or "more likely to generalize" than conclusions from single studies. However, this notion is not well formalized and formalizing this question is a goal of this project. In addition, existing meta-analysis methods do not take into account any knowledge of the similarities and differences between the studies. Taking advantage of these similarities and differences can improve the effectiveness of meta-analysis.This project takes advantage of recent developments in the area of "causal inference" which is the study inferring cause and effect relationships from data. These types of inferences utilizes a type of graph called a causal graph which graphically represents cause and effect relationships. This project develops an alternate framework for meta-analysis based on a novel type of causal graph, a selection graph. A selection graph formally represents the similarities and differences between the studies. This project provides a unifying framework and powerful powerful methodology for meta-analysis. The methods developed in this project are applied to genetic studies where meta-analyses have discovered thousands of variants involved in common human disease in the past few years.Causal graphs have had a major impact on the way causality is taught and understood in cognitive science, statistics, and the health and social sciences. The proposed research promises to have similar impacts by transforming the approach to meta-analysis, one of the work horses of statistical inference in the physical, life and social sciences. The resulting techniques will be used to perform meta-analyses of genetic studies which can lead to the discovery of variation involved in disease. The results of the project, including publications, software, data sets, and course materials will be made freely available through the project web site: http://zarlab.cs.ucla.edu/causal-meta-analysis/.
由于各种原因,包括研究的样本量小或研究人员不知道的混杂因素,研究的统计结论往往会产生误导。减少误导性结论可能性的一种方法是使用一种称为“荟萃分析”的技术将多项研究的结果结合起来。元分析是从科学数据中推断知识的最广泛使用的技术之一。荟萃分析研究背后的理念是,来自多个研究的综合统计结论反映了所有研究中的信息,并且更有可能是准确的。荟萃分析的结论被认为比单一研究的结论“更好”或“更有可能推广”。然而,这个概念还没有很好地形式化,而形式化这个问题是这个项目的目标。此外,现有的荟萃分析方法没有考虑到研究之间的相似性和差异性。利用这些异同可以提高meta分析的有效性。该项目利用了“因果推理”领域的最新发展,这是一项从数据中推断因果关系的研究。这些类型的推论利用一种称为因果图的图形来表示因果关系。本项目基于一种新型的因果图,即选择图,开发了一种元分析的替代框架。选择图正式表示研究之间的相似点和不同点。这个项目为元分析提供了一个统一的框架和强有力的方法论。在这个项目中开发的方法被应用到遗传研究中,在过去的几年里,荟萃分析已经发现了数千种与常见人类疾病有关的变异。因果图对认知科学、统计学、健康和社会科学中因果关系的教授和理解方式产生了重大影响。拟议的研究有望通过将方法转化为元分析(meta-analysis)来产生类似的影响,元分析是物理、生命和社会科学中统计推断的主力之一。由此产生的技术将用于对基因研究进行荟萃分析,从而发现与疾病有关的变异。该项目的成果,包括出版物、软件、数据集和课程材料将通过该项目的网址:http://zarlab.cs.ucla.edu/causal-meta-analysis/免费提供。

项目成果

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Eleazar Eskin其他文献

Improving the usability and archival stability of bioinformatics software
  • DOI:
    10.1186/s13059-019-1649-8
  • 发表时间:
    2019-02-27
  • 期刊:
  • 影响因子:
    9.400
  • 作者:
    Serghei Mangul;Lana S. Martin;Eleazar Eskin;Ran Blekhman
  • 通讯作者:
    Ran Blekhman
Systematic benchmarking of omics computational tools
组学计算工具的系统基准测试
  • DOI:
    10.1038/s41467-019-09406-4
  • 发表时间:
    2019-03-27
  • 期刊:
  • 影响因子:
    15.700
  • 作者:
    Serghei Mangul;Lana S. Martin;Brian L. Hill;Angela Ka-Mei Lam;Margaret G. Distler;Alex Zelikovsky;Eleazar Eskin;Jonathan Flint
  • 通讯作者:
    Jonathan Flint
Discrete profile comparison using information bottleneck
  • DOI:
    10.1186/1471-2105-7-s1-s8
  • 发表时间:
    2006-03-20
  • 期刊:
  • 影响因子:
    3.300
  • 作者:
    Sean O'Rourke;Gal Chechik;Robin Friedman;Eleazar Eskin
  • 通讯作者:
    Eleazar Eskin
MEF: Malicious Email Filter - A UNIX Mail Filter That Detects Malicious Windows Executables
MEF:恶意电子邮件过滤器 - 检测恶意 Windows 可执行文件的 UNIX 邮件过滤器
Dealing with large diagonals in kernel matrices
  • DOI:
    10.1007/bf02530507
  • 发表时间:
    2003-06-01
  • 期刊:
  • 影响因子:
    0.600
  • 作者:
    Jason Weston;Bernhard Schölkopf;Eleazar Eskin;Christina Leslie;William Stafford Noble
  • 通讯作者:
    William Stafford Noble

Eleazar Eskin的其他文献

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{{ truncateString('Eleazar Eskin', 18)}}的其他基金

III: Medium: Causal inference in biobanks: Leveraging genetics to infer causal relationships using electronic health records
III:中:生物库中的因果推断:利用电子健康记录利用遗传学来推断因果关系
  • 批准号:
    2106908
  • 财政年份:
    2021
  • 资助金额:
    $ 112.08万
  • 项目类别:
    Continuing Grant
III:Small: Replication Studies for High Dimensional Data: Insights into Confounding and Heterogeneity
III:小:高维数据的复制研究:洞察混杂和异质性
  • 批准号:
    1910885
  • 财政年份:
    2019
  • 资助金额:
    $ 112.08万
  • 项目类别:
    Continuing Grant
III: Medium: Detecting Low Dimensional Structures in Genomic Data
III:中:检测基因组数据中的低维结构
  • 批准号:
    1705197
  • 财政年份:
    2017
  • 资助金额:
    $ 112.08万
  • 项目类别:
    Standard Grant
III: Small: Causal and Statistical Inference in the Presence of Confounding Factors
III:小:存在混杂因素时的因果和统计推断
  • 批准号:
    1320589
  • 财政年份:
    2013
  • 资助金额:
    $ 112.08万
  • 项目类别:
    Standard Grant
BSF:2012304:Methods for Preprocessing Population Sequence Data
BSF:2012304:群体序列数据的预处理方法
  • 批准号:
    1331176
  • 财政年份:
    2013
  • 资助金额:
    $ 112.08万
  • 项目类别:
    Standard Grant
III: Medium: Private Identification of Relatives and Private GWAS: First Steps in the New Field of CryptoGenomics
III:媒介:亲属的私人身份识别和私人 GWAS:密码基因组学新领域的第一步
  • 批准号:
    1065276
  • 财政年份:
    2011
  • 资助金额:
    $ 112.08万
  • 项目类别:
    Standard Grant
III: Small: Inference of Causal Regulatory Relationships from Genetic Studies
III:小:从遗传研究中推断因果调节关系
  • 批准号:
    0916676
  • 财政年份:
    2009
  • 资助金额:
    $ 112.08万
  • 项目类别:
    Continuing Grant
Collaborative Research: Design and Analysis of Compressed Sensing DNA Microarrays
合作研究:压缩传感 DNA 微阵列的设计和分析
  • 批准号:
    0729049
  • 财政年份:
    2007
  • 资助金额:
    $ 112.08万
  • 项目类别:
    Continuing Grant
Collaborative Research: SEIII: Estimating Haplotype Frequencies
合作研究:SEIII:估计单倍型频率
  • 批准号:
    0731455
  • 财政年份:
    2007
  • 资助金额:
    $ 112.08万
  • 项目类别:
    Standard Grant
Collaborative Research: SEIII: Estimating Haplotype Frequencies
合作研究:SEIII:估计单倍型频率
  • 批准号:
    0513612
  • 财政年份:
    2005
  • 资助金额:
    $ 112.08万
  • 项目类别:
    Standard Grant

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