Nonparametric Methods for Clinical Predictive Model in Pharmacogenomics Research

描述（申请人提供）：药物基因组学研究中临床反应预测模型的非参数变量选择和降维全基因组基因表达信息已用于药物基因组学研究，以将患者的基因表达谱与药物疗效相关联。对于许多复杂的疾病，例如，癌症，预计基因表达谱将提供预测模型， 比基于标准临床特征的那些更精确，以定义患者特定的治疗策略。然而，发现影响药物反应的基因表达变异是复杂和具有挑战性的。计算困难包括全基因组基因表达数据是高维的，并且它们与药物反应的关系将是非线性的。因此，人们不能再依赖现有的统计和计算方法来充分分析数据。该项目的长期目标是开发统计和计算方法（用于分析高维但低样本量的数据），并将这些方法应用于药物基因组学研究。短期目标是专门开发非参数变量选择和降维技术，用于基因表达数据的临床反应预测模型。将追求三个具体目标：1）使用LOESS开发非参数瓦里选择方法（局部加权散点图平滑），其不假设临床应答的线性或任何其他特定形式的预测模型; 2）当维度远大于样本量时，将切片逆回归（SIR）扩展到降维问题，如药物基因组学中的情况; 3）将所提出的方法应用于药物基因组学（研究，其数据可在Gene Expression Omnibus（GEO）DataSets，：//www.example.com中获得www.ncbi.nlm.nih.gov/gds。本文提出的变量选择和降维方法对其他回归问题具有通用性，当回归函数没有特定的形式，数据很大，预测因子很高，但样本量相对较低时。执行分析的软件将使用统计软件包R语言，并将完全记录，以便于生物医学研究界使用。

DESCRIPTION (provided by applicant): Nonparametric Variable Selection and Dimension Reduction for Predictive Models of Clinical Response in Pharmacogenomics Research Whole genome gene expression information have been used in pharmacogenomics research to correlate patients' gene expression profiles with a drug's efficacy. For many complex diseases, e.g., cancers, it is anticipated that gene expression profiles will provide predictive models, more precise than those based on standard clinical features, to define patient-specific treatment strategies. However, finding gene expression variations that affect drug response is complicated and challenging. Computational difficulties include that the whole genome gene expression data are high dimensional and their relationships to drug response would be nonlinear. Therefore, one can no longer rely on existing statistical and computational methods to adequately analyze the data. The long-term objective of the proposed project is to develop statistical and computational methods (for analysis of high dimensional but low sample size data and apply the methods in pharmacogenomics research. The short-term objective is to specifically develop nonparametric variable selection and dimension reduction techniques for predictive models of clinical response on gene expression data.) Three specific aims will be pursued: 1) Develop nonparametric vari- able selection approaches using LOESS (locally weighted scatterplot smoothing), which does not assume linear or any other specific forms of predictive models for clinical response; 2) Ex- tend Sliced Inverse Regression (SIR) to dimension reduction problems when the dimension is much larger than the sample size, as the case in pharmacogenomics; 3) Apply the proposed methods in pharmacogenomics (studies, whose data are available in Gene Expression Omnibus (GEO) DataSets, ://www.ncbi.nlm.nih.gov/gds . The proposed variable selection and dimension reduction methods are general to other regression problems, when the regression functions do not have specific forms and the data are big in terms of very high dimensional predictors but relatively low sample size.) Software to implement analysis will use the statistical package R language and will be fully documented for easy use by the biomedical research community.