New Statistical Methods for Medical Signals and Images

医学信号和图像的新统计方法

• 批准号: 6687387
• 负责人: Iain M Johnstone
• 金额: $ 36.76万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-09-10 至 2007-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6687387
• 关键词: New; Statistical; Methods; Medical; Signals

DESCRIPTION (provided by applicant): Medical and biological data often come in the form of digitized signals and images; for example, mass spectrograms, electrocardiogram traces, human gait cycles, and even the representation of gene expression arrays. As instrumental data acquisition becomes routine, sequences of such images, signals or paths are collected, often along with other covariate measurements, resulting in datasets where the basic unit of measurement, or response, is a very high-dimensional object. The gene microarray is a leading example of how new technology has led to data acquisition on a massive scale; we also expect to work with more direct protein measurements obtained through mass spectrometry. The project continues to focus on developing techniques for modeling and understanding such data that naturally adapt to the high dimensionality. For regression and classification with gene expression arrays, we consider methods that are a subtle blend between univariate and multivariate, that offer both good prediction and gene selection. To study covariance structure, the project continues to develop "sparse" forms of principal components and discriminant analysis that may be more sensitive to either local phenomena of not necessarily smooth form or that are more adapted to irregularly observed data. Corresponding quadratically regularized methods in appropriate bases form a natural foil for comparison, and inference procedures for some of these are proposed. For estimation of means, the project will examine sparse empirical Bayes and False Discovery Rate methods for estimating non smooth local phenomena. Much of this work will be carried out in existing and new collaborations with researchers in oncology, genetics, cardiology and other specialties, working for example on cancer, heart disease and human locomotion.

描述（由申请人提供）：医学和生物数据通常以数字化信号和图像的形式出现；例如，质谱图、心电图轨迹、人类步态周期，甚至基因表达阵列的表示。随着仪器数据采集变得常规，此类图像、信号或路径的序列通常与其他协变量测量一起被收集，从而产生数据集，其中测量或响应的基本单位是非常高维的对象。基因微阵列是新技术如何实现大规模数据采集的一个典型例子。我们还期望通过质谱法获得更直接的蛋白质测量。该项目继续专注于开发自然适应高维的数据建模和理解技术。对于基因表达阵列的回归和分类，我们考虑单变量和多变量之间微妙混合的方法，这些方法可以提供良好的预测和基因选择。为了研究协方差结构，该项目继续开发主成分和判别分析的“稀疏”形式，这些分析可能对不一定平滑形式的局部现象更敏感，或者更适合不规则观察的数据。 在适当的基础上相应的二次正则化方法形成了用于比较的自然陪衬，并提出了其中一些方法的推理程序。为了估计平均值，该项目将检查稀疏经验贝叶斯和错误发现率方法来估计非平滑局部现象。其中大部分工作将通过与肿瘤学、遗传学、心脏病学和其他专业研究人员现有和新的合作来进行，例如研究癌症、心脏病和人类运动。