CAREER: Theory and Application of Small-Sample Error Estimation in Genomic Signal Processing

标题：基因组信号处理中的小样本误差估计理论与应用p.i: Ulisses Braga-NetoProject摘要基因组信号处理（GSP）是一门工程学科，研究与高通量技术测量的生物信号相关的建模和统计问题，如基因表达微阵列或蛋白质丰度质谱。gsp的研究通常涉及发现用于疾病诊断和预后的可靠分子标记，使用模式识别或机器学习方法。这种方法依赖于误差估计的准确性进行分类和预测。由于gsp应用程序中常见的小样本量，这一点尤其重要。在GSP中需要新的鲁棒小样本误差估计方法，以实现可重复的科学发现，从而导致真正的医学进步。本研究的目标是解决小样本误差估计中存在的重大计算和统计问题。将解决的开放问题包括：(1)获得线性连续分类器的估计误差和真实误差的联合抽样分布的精确和近似表示，这将导致性能更好的误差估计器和评估结果显著性的实用工具；(2)研究离散分类器的误差估计，包括二元确定系数（CoD），使用分析和完全枚举方法；(3)发展增强误差估计方法，解决在高维空间和自适应核中的应用，重点是特征选择；(4)与translationgenomics （TGen）、Johns Hopkins医学院和巴西OswaldoCruz Foundation （FIOCRUZ）的医学合作者合作，将这些误差估计技术应用于癌症和传染病的诊断和预后的生物标志物发现问题。

NSF Proposal #0845407Title: Theory and Application of Small-Sample Error Estimation in Genomic Signal ProcessingP.I.: Ulisses Braga-NetoProject AbstractGenomic Signal Processing (GSP) is the engineering discipline thatstudies modeling and statistical issues related to biological signalsmeasured by high-throughput technology, such as gene-expressionmicroarrays or protein-abundance mass spectrometry. Research in GSPtypically involve the discovery of reliable molecular markers fordisease diagnosis and prognosis, using pattern recognition or machinelearning approaches. Such approaches rely on the accuracy of errorestimation for classification and prediction. This is particularlycritical due to the small sample sizes that are common in GSPapplications. Novel robust small-sample error estimation methodologiesin GSP are needed in order to enable reproducible scientific discoverythat leads to genuine medical advancement.This research has as its goal solving significant computational andstatistical problems that exist in small-sample error estimation.Among the open problems that will be addressed are (1) to obtain exactand approximate representations of the joint sampling distribution ofthe estimated and true errors for linear continuous classifiers, whichwill lead to better-performing error estimators and practical tools toassess significance of results; (2) to study error estimation fordiscrete classifiers, including the binary coefficient ofdetermination (CoD), using both analytical and complete enumerationapproaches; (3) to develop the methodology of bolstered errorestimation, addressing the application in high-dimensional spaces andwith adaptive kernels, with an emphasis on feature selection; (4) toapply these error estimation techniques to the problem of biomarkerdiscovery for diagnosis and prognosis in cancer and infectiousdiseases, in partnership with medical collaborators at TranslationalGenomics (TGen), the Johns Hopkins Medical School, and the OswaldoCruz Foundation, Brazil (FIOCRUZ).