Statistical methods for large-scale significance and prediction analysis with app

使用应用程序进行大规模显着性和预测分析的统计方法

• 批准号: 7649099
• 负责人: Baolin Wu
• 金额: $ 13.9万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-07 至 2011-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7649099
• 关键词: Address; Beds; Biological; Biological Markers; Cancer Patient; Cancer Research Project; Charge; Communities; Computer software; Data; Databases; Dependence; Detection; Dimensions; Early treatment; Gene Expression; Genes; Heterogeneity; Internet; Literature; Logistic Regressions; Malignant Neoplasms; Methods; Modeling; Molecular; Molecular Diagnosis; Pathway interactions; Phenotype; Public Health; Research; Sample Size; Sampling; Statistical Data Interpretation; Statistical Methods; Technology; Testing; base; cancer microarray; design; flexibility; gene function; gene interaction; improved; leukemia; molecular marker; novel; outcome forecast; predictive modeling; software development; statistics; user-friendly

Current technology advances have brought us massive biomedical data for statistical analysis, for example, the cancer microarray data. Typical of these data is the common feature that the number of observed samples is much smaller than the number of variables/predictors, which poses challenges for statistical analysis. Identifying differentially expressed genes and predicting sample phenotype based on the gene expressions data are two important research questions in analyzing these large-scale biomedical data. This project proposes to develop some new large-scale prediction and signifiance analysis statistical methods that are specially designed to address small sample size and potential sampe heterogeneity issues, incorporate existing biological information for improved inference, and can be applied very generally. The usefulness of these methods will be shown with the large-scale biomedical data originating from the leukemia cancer research projects. The cancer projects aimed to improve the cancer molecular diagnosis and prognosis by identifying molecular biomarkers for critical early treatment and rapid, noninvasive testing. The specific aims are 1) Develop new statistical methods for significance testing of large-scale molecular markers. 2) Develop new statistical methods that appropriately model the sample heterogeneity for significance testing. 3) Develop new statistical methods that utilize the gene group information to improve cancer prediction. 4) Use the developed models and methods to answer research questions relevant to public health in the leukemia cancer projects; and implement and validate the proposed methods in user-friendly and well-documented software, and distribute them to the scientific community at no charge. Project

当前的技术进步为我们带来了大量的生物医学数据进行统计分析，例如，癌症微阵列数据。这些数据的典型特征是观测样本的数量远小于变量/预测因子的数量，这给统计分析带来了挑战。在大规模生物医学数据分析中，识别差异表达基因和基于基因表达数据预测样本表型是两个重要的研究问题。 本计画提出发展一些新的大规模预测与显著性分析统计方法，这些统计方法是特别设计来解决小样本量与潜在样本异质性的问题，并结合现有的生物资讯以改善推论，且可以非常普遍地应用。这些方法的有效性将显示与大规模的生物医学数据来源于白血病癌症研究项目。这些癌症项目旨在通过识别用于关键早期治疗和快速非侵入性检测的分子生物标志物来改善癌症分子诊断和预后。 具体目标是：1）发展新的大规模分子标记显著性检验的统计方法。2)开发新的统计方法，适当地模拟样本异质性进行显著性检验。3)开发新的统计方法，利用基因组信息来改善癌症预测。4)使用开发的模型和方法来回答白血病项目中与公共卫生相关的研究问题;并在用户友好和记录良好的软件中实施和验证所提出的方法，并将其免费分发给科学界。项目