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）：该提案开发了新的统计方法，以从高通量数据（如来自癌症研究的微阵列和蛋白质组数据）中选择一小组分子。这项研究的挑战是这些研究中继承的非线性维度，特别是当引入基因-基因相互作用时。该维度对统计计算、方法学发展和理论研究都有很大的影响。挑战将通过使用所提出的新颖的独立筛选方法来处理，该方法还解决了计算需求和稳定性，以及超高维统计推断中的随机误差积累问题。一个迭代的独立筛选方法被引入到寻找隐藏的签名基因，是微不足道的，但共同非常重要的临床结果。它还使我们能够消除冗余分子，这些分子与临床结果的相关性很小，但联合很弱。随着特征的数量减少到一个可管理的水平，惩罚伪似然方法将被引入到进一步选择相关基因。此外，还介绍了寻找分子协同基团的方法。独立筛选及其迭代版本的想法将被应用于各种统计问题，从高通量数据的分析，从多维回归和分类到生存时间的分析，全基因方差的估计和微阵列的归一化。所提出的方法的有效性将通过渐近理论和模拟研究进行评估。正在进行的癌症生物医学研究的数据集，如乳腺癌，多发性骨髓瘤，神经母细胞瘤，肺肿瘤和肝癌将使用新开发的统计和生物信息学工具进行批判性分析。公共卫生关系：超高维生物医学数据的统计方法PI：Jianqing Fan该提案开发了新的统计和生物信息学工具，用于发现与临床结果相关的基因和蛋白质。正在进行的癌症生物医学研究的数据集，如乳腺癌，多发性骨髓瘤，神经母细胞瘤，肺肿瘤和肝癌将使用新开发的统计和生物信息学工具进行批判性分析。研究结果将对理解癌症的分子机制和开发治疗靶点产生重大影响。