Semiparametric Models for Large Scale-Biomedical Data

大规模生物医学数据的半参数模型

• 批准号: 7171900
• 负责人: Jianqing Fan
• 金额: $ 18.56万
• 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 2010-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7171900
• 关键词: Acquired Immunodeficiency Syndrome; Address; Affect; Biological; Biological Process; Cell physiology; Cells; China; Classification; Cohort Studies; Computer software; Condition; DNA analysis; Data; Data Set; Diagnosis; Disease; Drug Design; Gene Expression; Gene Proteins; Genes; Goals; Harvest; Health; Human; Individual; Inherited; Investigation; Longitudinal Studies; Malignant Neoplasms; Medical; Methodology; Methods; Micro Array Data; Microarray Analysis; Migration Inhibitory Factor; Modeling; Molecular; Molecular Profiling; Neuroblastoma; Nutrition Surveys; Oncogenic Viruses; Pattern; Pharmaceutical Preparations; Pharmacologic Substance; Proteomics; Rate; Risk Factors; SARS coronavirus; Scientist; Statistical Methods; Structure; Systematic Bias; Techniques; Therapeutic; Time; Variant; Virus Diseases; disease classification; improved; innovation; innovative technologies; novel; novel strategies; outcome forecast; phenylpyruvate tautomerase; protein expression; research study; response; simulation; tool; tumor

DESCRIPTION (provided by applicant): This.aim of this proposal is to develop novel statistical methodology to address issues in the analysis of large scale data from biomedical studies, especially the studies of tumors and virus diseases. The problems arising from the analysis of DNA micorarray, proteomic and longitudinal data will be carefully investigated. The proposal focuses on developing innovative semiparametric techniques for removing systematic biases in microarray experiments, selecting significantly expressed patterns of genes and proteins at different time points and under different experimental conditions, and efficiently assessing the covariate effects and predicting individual response trajectory for longitudinal studies. The strength and weakness of each proposed method will be critically scrutinized via theoretical investigations and simulation studies. Related software will be developed. Data sets from ongoing biological studies on cancer and virus diseases will be analyzed by using the newly developed statistical methods. This study allows biologists to more effectively remove the impact of experimental variations inherited in microarray experiments and permits biologists to reveal more meaningful scientific results with lower false discovery rates. It provides cutting-edge tools for biologists to understand biological processes, molecular functions and cellular activities. It introduces new tools for medical scientists to unveil how the risk factors affect individual disease over time. These will result in improved disease classification, diagnosis, prognosis, and drug design, among other pharmaceutical, therapeutic and medical goals.

描述（由申请人提供）：本提案的目的是开发新的统计方法，以解决生物医学研究（特别是肿瘤和病毒性疾病研究）大规模数据分析中的问题。从DNA微阵列，蛋白质组学和纵向数据的分析所产生的问题将仔细研究。该提案的重点是开发创新的半参数技术，以消除微阵列实验中的系统性偏倚，选择在不同时间点和不同实验条件下显著表达的基因和蛋白质模式，并有效地评估协变量效应和预测纵向研究的个体响应轨迹。每种方法的优点和缺点将通过理论研究和模拟研究进行严格审查。将开发相关软件。正在进行的癌症和病毒性疾病生物学研究的数据集将使用新开发的统计方法进行分析。这项研究使生物学家能够更有效地消除微阵列实验中遗传的实验变异的影响，并使生物学家能够以更低的错误发现率揭示更有意义的科学结果。它为生物学家了解生物过程，分子功能和细胞活动提供了尖端工具。它为医学科学家提供了新的工具，以揭示风险因素如何随着时间的推移影响个体疾病。这些将导致改善疾病分类、诊断、预后和药物设计，以及其他制药、治疗和医疗目标。