NEW STATISTICAL METHODS FOR MEDICAL SIGNALS AND IMAGES

医疗信号和图像的新统计方法

• 批准号: 6513032
• 负责人: Iain M Johnstone
• 金额: $ 23.84万
• 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 2003-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6513032
• 关键词: bioimaging /biomedical imaging; computer data analysis; diagnosis design /evaluation; digital imaging; electrocardiography; functional magnetic resonance imaging; human data; mathematical model; positron emission tomography; protein sequence; statistics /biometry

Medical and biological data often come in the form of digitized signals and images; for example, magnetic resonance images, electrocardiogram traces and even the folding paths of proteins. 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 high-dimensional object. The project continues to focus on developing techniques for modelling and understanding such data that explicitly take into account, and indeed exploit inherent spatial or temporal correlation, and when appropriate, relate it to covariate or class label information. To study covariance structure, the project proposes "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 will also be developed in certain applications. For estimation of means, the project will examine sparse empirical Bayes methods for estimating non smooth local phenomena. Much of this work will be carried out in existing and new collaborations with researchers in medical imaging, cardiology and other specialties, working for example on cancer, heart disease and brain mapping.

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，磁共振图像，心电图轨迹，甚至蛋白质的折叠路径。随着仪器数据采集成为常规，通常沿着其他协变量测量来收集这些图像、信号或路径的序列，从而产生其中基本测量单位或响应是高维对象的数据集。 该项目继续侧重于开发建模和理解此类数据的技术，这些技术明确考虑到并利用内在的空间或时间相关性，并酌情将其与协变量或类别标签信息联系起来。 为了研究协方差结构，该项目提出了“稀疏”形式的主成分和判别分析，这可能对不一定平滑形式的局部现象更敏感，或者更适合于不规则观测数据。 相应的二次正则化方法在适当的基础上形成了一个自然的箔比较，也将在某些应用中发展。对于平均值的估计，该项目将研究估计非光滑局部现象的稀疏经验贝叶斯方法。 其中大部分工作将与医学成像、心脏病学和其他专业的研究人员进行现有和新的合作，例如癌症、心脏病和大脑绘图。

项目成果

Modeling and superposition of multiple protein structures using affine transformations: analysis of the globins.

使用仿射变换对多个蛋白质结构进行建模和叠加：珠蛋白分析。

• 发表时间: 1998
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Wu,TD;Schmidler,SC;Hastie,T;Brutlag,DL
• 通讯作者: Brutlag,DL

Cell type-specific gene expression differences in complex tissues.

• DOI: 10.1038/nmeth.1439
• 发表时间: 2010-04
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Shen-Orr, Shai S.;Tibshirani, Robert;Khatri, Purvesh;Bodian, Dale L.;Staedtler, Frank;Perry, Nicholas M.;Hastie, Trevor;Sarwal, Minnie M.;Davis, Mark M.;Butte, Atul J.
• 通讯作者: Butte, Atul J.

Regression analysis of multiple protein structures.

多种蛋白质结构的回归分析。

• DOI: 10.1089/cmb.1998.5.585
• 发表时间: 1998
• 期刊: Journal of computational biology : a journal of computational molecular cell biology.
• 作者: Wu,TD;Schmidler,SC;Hastie,T;Brutlag,DL
• 通讯作者: Brutlag,DL