Collaborative Research: Sufficient Dimension Reduction for High Dimensional Data with Applications in Bioinformatics

合作研究：高维数据的充分降维及其在生物信息学中的应用

• 批准号: 0405681
• 负责人: Bing Li
• 金额: $ 26.9万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0405681&HistoricalAwards=false
• 关键词: Collaborative; Research; Sufficient; Dimension; Reduction

Abstract proposals: 0405360 and 0405681PIs: Cook & LiCOLLABORATIVE RESEARCH: Dimension Reduction with application to bioinformaticsAs represented in the existing literature, sufficient dimension reduction (SDR) encompasses model-free methods for linearly reducing the dimension of the predictor vector in regression and classification problems without loss of information. SDR methodology has a brief but striking record of success, although its inferential foundations are relatively narrow and the restriction to linear reductions can be limiting in some applications. The investigators and their co-authors expand the inferential foundations of SDR through the development of optimal methods within the context of linear reduction and the study of new nonlinear reduction methods. The new optimal reduction methods permit the investigators to derive model-free tests of conditional independence, which are roughly data-analytic equivalents of t-tests on coefficients in model-based linear regression. They emphasize bioinformatics applications in general and the analysis of data from high-throughput genomic technologies in particular.The computer revolution has produced an unprecedented capacity for data generation, processing and storage, with the consequence that data reduction is paramount in many research areas and business applications. For instance, genomic technology can produce measurements for thousands of genes across multiple tissue samples, and WalMart makes over 20 million transactions daily. The development of diagnostics for breast cancer based on fine needle aspiration can involve the study of numerous measurements on extracted cells across hundreds of patients. In response to this proliferation of information, the investigators and their colleagues study methods for reducing data to an essential core. Their approach is unique because their overarching goal is reduction without loss of information on the issues under consideration. In the development of diagnostics for breast cancer, this goal translates into reducing numerous cell measurements to an index that can be used to classify a breast mass as malignant or benign without loss of information, allowing the physician to present a more informed recommendation to the patient.

摘要提案：0405360和0405681 PI：Cook& Li合作研究：降维及其在生物信息学中的应用如现有文献所示，充分降维（SDR）包括无模型方法，用于线性降低回归和分类问题中预测向量的维数，而不会丢失信息。 特别提款权方法有一个简短但引人注目的成功记录，尽管其推理基础相对狭窄，并且在某些应用中对线性减少的限制可能有限。 研究人员和他们的合著者通过在线性约简和新的非线性约简方法的研究范围内开发最优方法来扩展SDR的推理基础。 新的最优降阶方法允许研究人员推导出条件独立性的无模型检验，这大致相当于基于模型的线性回归系数的t检验的数据分析。 它们一般强调生物信息学的应用，特别强调对高通量基因组技术数据的分析，计算机革命产生了前所未有的数据生成、处理和存储能力，其结果是，数据简化在许多研究领域和商业应用中至关重要。 例如，基因组技术可以测量多个组织样本中的数千个基因，沃尔玛每天的交易量超过2000万笔。基于细针抽吸的乳腺癌诊断的发展可能涉及对数百名患者的提取细胞的大量测量的研究。为了应对这种信息的扩散，研究人员和他们的同事研究了将数据减少到基本核心的方法。他们的方法是独特的，因为他们的总体目标是减少而不丢失有关所审议问题的信息。 在乳腺癌诊断的发展中，这一目标转化为将大量细胞测量结果减少到可用于将乳腺肿块分类为恶性或良性而不丢失信息的指数，从而允许医生向患者提供更明智的建议。