Nonparametric Methods for Clinical Predictive Model in Pharmacogenomics Research

描述(申请人提供)：用于药物基因组学临床反应预测模型的非参数变量选择和降维研究已将全基因组基因表达信息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、更多 比基于标准临床特征的治疗更精确，以定义患者特定的治疗策略。然而，寻找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，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。拟议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用于基于基因表达数据的临床反应预测模型的非参数变量选择和降维技术。)将追求三个具体目标：1)使用LOISE(局部加权散点图平滑)开发非参数可变选择方法，其不假定线性或任何其他特定形式的临床反应预测模型；2)扩展切片逆回归(SIR)以在维度远大于样本大小的情况下进行降维问题，如在药物基因组学中；3)将建议的方法应用于药物基因组学(如药物基因组学)；3)将建议的方法应用于药物基因组学(其数据可在基因表达总览(GEO)数据库中获得，网址为：//www.ncbi.nlm.nih.gov/gds)。所提出的变量选择和降维方法适用于其他回归问题，当回归函数没有特定的形式且数据量很大时，预测因子的维度很高，但样本量相对较小。实施分析的软件将使用统计包R语言，并将被完整地记录下来，以便于生物医学研究社区使用。

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.