Collaborative Research: Small-Sample Error Estimation for Classification with Application to Genomic Signal Processing

合作研究：小样本分类误差估计及其在基因组信号处理中的应用

• 批准号: 0634794
• 负责人: Edward Dougherty
• 金额: $ 22.02万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0634794&HistoricalAwards=false
• 关键词: Collaborative; Research; Small; Sample; Error

The availability of DNA microarray chips and other high-throughput technologies for measuring genomic variables fosters the hope that engineering can successfully address a key problem of translational genomics: using genomic signals to classify disease. Classification can serve to diagnosis the existence or category of a particular pathology or it can be used to prognosticate the effect of a treatment. In cancer, diagnosis can be between different stages of tumor development, and prognosis can be to predict the toxicity or benefit of a drug relative to the particular genetic make-up of an individual an example of personalized medicine. Error estimation is critical because the error of a classifier determines its worth. Gene-based classification typically involves small samples (numbers of microarrays), so that the same data must be used to train and test a classifier. Error estimators that work on the training data tend to suffer from low bias or high variance. This research improved the performance and widens the range of applicability of three recently proposed small-sample estimation paradigms: (1) bolstered error estimators place a kernel at each sample point and then apply the designed classifier to the distribution formed from the bolstering kernels to estimate its error; (2) convex error estimators are formed by an optimal weighted average of low- and high-biased estimators; and (3) calibrated error estimators are formed by using the data to optimally calibrate standard error estimators. This research generalizes the estimation rules, provides methods to obtain estimator parameters, and applies the estimators to genomic diagnosis and prognosis. Properties are mainly studied via simulation: however, analytic results are derived in cases where the error-estimator distributions are known.

DNA微阵列芯片和其他用于测量基因组变量的高通量技术的可用性促进了工程可以成功解决翻译基因组学的一个关键问题的希望：使用基因组信号对疾病进行分类。分类可用于诊断特定病理的存在或类别，或者可用于说明治疗效果。在癌症中，诊断可以在肿瘤发展的不同阶段之间进行，预后可以是预测药物相对于个体特定遗传组成的毒性或益处，这是个性化医疗的一个例子。错误估计是至关重要的，因为分类器的错误决定了它的价值。基于基因的分类通常涉及小样本（微阵列的数量），因此必须使用相同的数据来训练和测试分类器。在训练数据上工作的误差估计器往往会受到低偏差或高方差的影响。 本研究改进了最近提出的三种小样本估计范式的性能并拓宽了其适用范围：（1）支持误差估计器在每个样本点放置一个核，然后将设计的分类器应用于由支持核形成的分布以估计其误差：（2）凸误差估计器由低偏估计器和高偏估计器的最优加权平均形成;以及（3）通过使用数据最优地校准标准误差估计量来形成校准误差估计量。本研究归纳了估计规则，给出了估计参数的获取方法，并将其应用于基因组诊断和预后。属性主要是通过模拟研究：然而，分析结果推导出的情况下，误差估计分布是已知的。