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

摘要建议：0405360和0405681 PI：Cook&Amp；LiCOLLABORATIVE研究：应用于生物信息学的降维方法如现有文献中所示，充分降维包含了在不丢失信息的情况下线性降低回归和分类问题中预测向量的维度的无模型方法。特别提款权方法有着短暂但引人注目的成功记录，尽管其推理基础相对较窄，在某些应用中对线性减法的限制可能是有限的。研究人员和他们的合著者通过在线性约简的背景下开发最优方法和研究新的非线性约简方法来扩展SDR的推理基础。新的最优约简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中系数的t检验的数据分析等价。它们一般强调生物信息学应用，特别是对来自高通量基因组技术的数据的分析。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，在许多研究领域和商业应用中，数据简化是至关重要的。例如，基因组技术可以对多个组织样本中的数千个基因进行测量，沃尔玛每天的交易量超过2000万笔。基于细针吸取的乳腺癌诊断方法的发展可能涉及对数百名患者提取的细胞进行大量测量的研究。为了应对这种激增的信息，研究人员和他们的同事研究了将数据减少到关键核心的方法。他们的做法是独一无二的，因为他们的总体目标是在不损失所审议问题的信息的情况下减少开支。在乳腺癌诊断的发展中，这一目标转化为将大量细胞测量转化为一种指数，该指数可用于在不丢失信息的情况下将乳腺肿块分类为恶性或良性，从而使医生能够向患者提供更明智的建议。

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.