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：
10734451
负责人：
Iain M Johnstone
金额：
$ 49.69万
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 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10734451
关键词：
Algorithms Awareness Basic Science Binding Biology Biopsy Cells Collection Computational Biology Computer software DNA Sequence Data Data Analyses Development Disadvantaged Disease Disparate EEG-based imaging Evolution Exhibits Gene Expression Genetic Genetic Variation Genomics Goals Grant Height Human Image Immune Immunology Individual Label Lasso Lead Learning Linkage Disequilibrium Mass Spectrum Analysis Measures Medical Medicine Methods Modeling Names Outcome Patients Pattern Persons Phenotype Protein Array Reporting Research Research Personnel Signal Transduction Statistical Methods Statistical Models Statistical Study Structure Students Techniques Testing Tissues Trees Uncertainty Validation Viral algorithm development biobank clinical investigation experience genetic analysis genome wide association study high dimensionality innovation interest large datasets learning strategy magnetic resonance imaging/electroencephalography multidimensional data novel strategies novel therapeutics phenome pleiotropism population health simulation statistics supervised learning theories tool trait transcriptome sequencing

项目摘要

The analysis of large datasets from computational biology and medicine represents an important chal- lenge for Statisticians. These biomedical data typically have a large number of correlated features with rel- atively weak signals for predicting phenotypes of interest. Examples include DNA sequences and GWAS, mass-spectra, RNAseq and protein arrays. The broad goal of this ongoing three-investigator grant is to de- velop and study statistical techniques that enhance the analysis and interpretation of these data. The team combines experience in statistical modeling, algorithmic development, and theoretical analysis. Through four Specific Aims, the new projects focus on development and validation of state-of-the art statistical methods to use structure to learn from high-dimensional data to advance human population health. 1. Cluster-aware supervised learning. In “omics” settings, there are a large number of features that often exhibit sizable correlations. This aim proposes the Cluster-Aware Lasso, a statistical method which fits a lasso regression model that adaptively selects clusters of features using a hierarchical clustering-based approach, enforcing a notion of a tree-respecting solution. It will be validated on gene expression and mass spec data, and extensions to other supervised learning settings studied. 2. SNP Selection from GWAS summary statistics with FDR control. Genome-wide association studies often report findings for phenotypes in terms of summary statistics for individual SNPs. This aim develops a statistical method to identify causal SNPs while controlling the False Discovery Rate. It uses an estimate of the SNP correlation matrix based on linkage-disequilibrium data, an approximate multivariate lasso fit and model-X knockoff techniques, and will be validated on UK Biobank data. 3. Inference for high-dimensional genetic covariance matrices. Statistical estimation of large genetic covariance matrices is needed to learn whether genetic variation at phenome-wide scale is concen- trated in relatively few trait combinations, with implications for evolution and pleiotropy. This aim will explore biases in Restricted Maximum Likelihood and study alternative parametric and nonparametric methods of estimation both by asymptotic approximation and simulation. 4. Mixture lasso for multiple instance learning. It is often known whether a person is sick, but not which of their immune cells are responding to a particular illness, nor which parts of biopsied tissue are diseased. There is a label only for each patient, but data instances on a more granular level. The aim is to predict the labels of each data instance. This project proposes a supervised learning method based on mixtures and the lasso, with validation on viral sequence and mass spectrometry data. 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.

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

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Post model-fitting exploration via a "Next-Door" analysis.

DOI：
10.1002/cjs.11542
发表时间：
2020-09
期刊：
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
影响因子：
0.6
作者：
Guan, Leying;Tibshirani, Robert
通讯作者：
Tibshirani, Robert

Significant sparse polygenic risk scores across 813 traits in UK Biobank.

DOI：
10.1371/journal.pgen.1010105
发表时间：
2022-03
期刊：
PLoS genetics
影响因子：
4.5
作者：
Tanigawa Y;Qian J;Venkataraman G;Justesen JM;Li R;Tibshirani R;Hastie T;Rivas MA
通讯作者：
Rivas MA

Roy's largest root under rank-one perturbations: the complex valued case and applications.

罗伊在一级扰动下的最大根源：复杂的有价值的案例和应用。