Statistical Methods for Ultrahigh-dimensional Biomedical Data

超高维生物医学数据的统计方法

• 批准号: 7714616
• 负责人: Jianqing Fan
• 金额: $ 26.68万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-02-01 至 2014-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7714616
• 关键词: Address; Bioinformatics; Carcinogens; Cells; Classification; Clinical; Cluster Analysis; Computer software; Cox Proportional Hazards Models; Data; Data Set; Development; Gene Proteins; Genes; Harvest; Human; Inherited; Investigation; Linear Models; Liver; Lung Neoplasms; Malignant Neoplasms; Methods; Migration Inhibitory Factor; Modeling; Molecular; Multiple Myeloma; Necrosis; Neuroblastoma; Outcome; Pharmaceutical Preparations; Proteins; Proteomics; Quality Control; Regression Analysis; Research; Sampling; Screening procedure; Statistical Methods; Survival Analysis; Techniques; Theoretical Studies; Therapeutic; Time; Toxicogenomics; anticancer research; gene interaction; high throughput analysis; innovative technologies; malignant breast neoplasm; member; novel; novel strategies; phenylpyruvate tautomerase; protein expression; public health relevance; simulation; theories; therapeutic target; tool

DESCRIPTION (provided by applicant): This proposal develops novel statistical methods to select a small group of molecules from high-throughput data such as microarray and proteomic data from cancer research. The challenge of the study is the ultrahigh dimensionality inherited in these studies, particular when gene-gene interactions are introduced. The ultrahigh dimensionality has large impact on statistical computation, methodological developments, and theoretical studies. The challenge will be dealt by using the proposed novel independence screening methods, which also addresses the computational demand and stability, and the issues of stochastic error accumulation in ultra-high dimensional statistical inferences. An iterative independence screening method is introduced to find hidden signature genes that are marginally unimportant but jointly extremely important to the clinical outcomes. It also enables us to eliminate redundant molecules that are marginally highly but jointly weakly associated with clinical outcomes. With number of features surely reduced to a manageable level, penalized pseudo-likelihood methods will be introduced to further select relevant genes. In addition, methods for finding synergetic groups of molecules are introduced. The idea of independence screening and its iterated version will be applied to various statistical problems from the analysis of high throughput data, ranging from ultrahigh dimensional regression and classification to the analysis of survival time, estimation of genewide variance, and normalization of microarrays. The efficacy of the proposed methods will be evaluated via asymptotic theory and simulation studies. Data sets from on-going biomedical studies on cancer such as breast cancer, multiple myeloma, neuroblastoma, lung tumor, and liver carcigogen will be critically analyzed using the newly developed statistical and bioinformatic tools. PUBLIC HEALTH RELEVANCE: Statistical Methods for Ultrahigh-dimensional Biomedical Data PI: Jianqing Fan This proposal develops novel statistical and bioinformatic tools for finding genes and proteins that are associated with clinical outcomes. Data sets from on-going biomedical studies on cancer such as breast cancer, multiple myeloma, neuroblastoma, lung tumor, and liver carcinogen will be critically analyzed using the newly developed statistical and bioinformatic tools. The research findings will have strong impact on understanding molecular mechanisms of cancer and developing therapeutic targets.

描述（由申请人提供）：该提案开发了新颖的统​​计方法，从高通量数据（例如微阵列和蛋白质组学数据）中从癌症研究中选择一小部分分子。该研究的挑战是这些研究中遗传的超高维度，尤其是当引入基因 - 基因相互作用时。超高维度对统计计算，方法论发展和理论研究具有很大的影响。挑战将通过使用拟议的新型独立性筛选方法来解决，该方法还解决了计算需求和稳定性，以及在超高维统计推断中积累的随机误差问题。引入了一种迭代独立性筛选方法，以找到隐藏的签名基因，这些基因略有不重要但对临床结果非常重要。它还使我们能够消除冗余分子，这些分子略有差异，但与临床结局相关。随着数量肯定会降低到可管理的水平，将引入受惩罚的伪样方法，以进一步选择相关基因。另外，引入了寻找协同分子组的方法。独立性筛选及其迭代版本的想法将应用于各种统计问题，从高吞吐量数据的分析（从超高维度回归和分类到生存时间的分析，Genewide方差的估计以及微阵列的归一化）。提出方法的功效将通过渐近理论和仿真研究评估。正在进行的有关癌症的生物医学研究（例如乳腺癌，多发性骨髓瘤，神经母细胞瘤，肺肿瘤和肝癌）的数据集将使用新开发的统计和生物信息学工具进行严格分析。公共卫生相关性：超高维生物医学数据的统计方法PI：江式粉丝该提案开发了新型的统计和生物信息学工具，用于查找与临床结果相关的基因和蛋白质。正在进行的有关癌症的生物医学研究（例如乳腺癌，多发性骨髓瘤，神经母细胞瘤，肺肿瘤和肝癌）的数据集将使用新开发的统计和生物信息学工具进行严格分析。研究发现将对理解癌症的分子机制和发展治疗靶标有强烈的影响。