权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

New Statistical Methods for Medical Signals and Images

医学信号和图像的新统计方法

基本信息

批准号：
10440353
负责人：
Iain M Johnstone
金额：
$ 49.27万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
1996
资助国家：
美国
起止时间：
1996-09-10 至 2023-09-20
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10440353
关键词：
Address Algorithms Architecture Basic Science Biological Biological Assay Biological Markers Biology Categories Cells Chromatin Modeling Chromosomes Collaborations Computational Biology Computer software DNA Sequence DNA Structure Data Data Analyses Data Set Development Disease EEG-based imaging Genes Genetic Genome Genomics Goals Grant Hi-C Image Lasso Lead Maps Measurement Measures Medical Medicine Methodology Methods Modeling Modernization Names Nature Pathway interactions Pattern Phenotype Property Protein Array Protein Family Publishing Quantitative Genetics Reporting Research Research Personnel Resolution Sampling Side Signal Transduction Statistical Methods Statistical Models Statistical Study Structure Students Techniques Uncertainty Variant Viral Proteins algorithm development base biomarker signature clinical investigation data privacy experience genome wide association study genome-wide analysis high dimensionality improved interest large datasets magnetic resonance imaging/electroencephalography multidimensional data novel strategies novel therapeutics outcome prediction theories tool trait transcriptome sequencing user friendly software virology

项目摘要

The analysis of large datasets from computational biology and medicine represents an important chal- lenge for Statisticians. These data typically have a large number of correlated features with relatively weak signals for predicting phenotypes of interest. Examples of such data includes DNA sequences and GWAS, mass-spectra, MRI and EEG images, RNAseq and protein arrays, to name a few. The broad goal of this ongoing three-investigator grant is to develop and study statistical techniques that enhance the analysis and interpretation of these data. Our team combines experience in statistical modeling, algorithmic devel- opment, and theoretical analysis of these techniques. In the new projects, our focus is the development of state-of-the art methods to exploit known or implied structure in order to extract useful information from high-dimensional data. The renewal will address these goals through four Speciﬁc Aims. The investigators will study: 1. Principal curves for modeling chromatin architecture. We propose new statistical methodology for modeling the chromatin structure of DNA based on contact maps derived from Hi-C assays. We use techniques inspired by principal curves, but applied in the context of metric scaling, that take into account local structure along the chromosome. 2. Fitting sparse models to large data and to summary data. Many modern datasets (e.g. GWAS with 1M SNPs and 500K subjects) are computationally challenging. We propose computational advances that enable the lasso to scale to such scenarios. Often the authors of published GWAS studies do not share the raw data for privacy and other reasons. We propose techniques for approximately ﬁtting multivariate versions of these models given only the univariate summary scores typically reported. 3. Estimating high-dimensional eigenstructure in virology and genetics. We will exploit low rank struc- ture in sequence data to compare different methods for inference about sectors in viral proteins. For quantitative genetics, we will develop statistical theory, methods and software for eigenanalysis of multiple levels of variation, and speciﬁcally for genetic covariance matrices. 4. Prediction with side information. Many studies seek biomarker signatures that are predictive of outcomes such as disease status under various treatments. We propose a statistical approach for exploiting side information such as membership in gene pathways or quantitative measures for each biomarker in order to increase the power for discovering signatures in these challenging domains. Working together, the investigators and their students will implement the new statistical tools into publi- cally available software, following a pattern established in earlier cycles of this grant, in which our packages have found wide use among medical researchers both at Stanford and around the world.

对来自计算生物学和医学的大型数据集的分析是一个重要的挑战