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Modeling, Inference, and Optimization for Genomic and Biomedical Big Data

基因组和生物医学大数据的建模、推理和优化

基本信息

批准号：
10633126
负责人：
Kenneth L Lange
金额：
$ 53.92万
依托单位：
UNIVERSITY OF CALIFORNIA LOS ANGELES
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-05-31
项目状态：
未结题

项目摘要

Abstract The biomedical sciences are drowning in big data. Progress in ﬁelds such as genomics and medical imaging is being stymied by the lack of ap- propriate computational tools. This grant promotes the development of algorithms, statistical methods, and software for the analysis of the big datasets encountered in the biomedical sciences. The NIH All of Us Pro- gram, the Million Veteran Project (MVP) sponsored by US Department of Veterans Affairs (VA), and the UK Biobank are three prime examples of recent massive datasets. These datasets require terabytes of storage on sample sizes ranging from 105 to 106 and above subjects. The datasets are also dynamic, growing over time in size and complexity. In addition, the datasets are heterogeneous; for example, the UK Biobank offers ge- nomic data, electronic health record (EHR) data, and imaging data on the same study individuals. Finally, as with most real-world data, the data are fraught with missingness and inaccuracy. We propose attacking the issues of parameter estimation and model selection raised by such massive datasets. We will be guided by princi- ples of parsimony and high-dimensional optimization. Most of the speciﬁc applications we have in mind involve imaging and genomics, particularly genomewide association discovery. Fortunately, most of the tools and soft- ware we construct will be more generically useful. Our successful algo- rithms will be coded in the modern scientiﬁc programming language Julia and posted on publicly available websites. We will focus on constrained and sparse regression, EM and MM algorithms for optimization, variance components models, bootstrapping of linear mixed models, a copula-like model for correlated data, and sensitivity analysis in epidemic models. These are all subjects of paramount importance in modern genomics, bio- statistics and data mining.

抽象的生物医学科学正被大数据淹没。等领域的进展由于缺乏应用程序，基因组学和医学成像正在受到阻碍适当的计算工具。这笔赠款促进了发展用于分析大数据的算法、统计方法和软件生物医学科学中遇到的数据集。 NIH 我们所有人都支持 - gram，美国国防部赞助的百万退伍军人项目（MVP）退伍军人事务部 (VA) 和英国生物银行是三个主要例子最近的海量数据集。这些数据集需要 TB 级的存储空间样本量范围为 105 至 106 及以上受试者。数据集它们也是动态的，其规模和复杂性随着时间的推移而不断增长。此外，数据集是异构的；例如，英国生物银行提供ge- 经济数据、电子健康记录 (EHR) 数据和影像数据相同的研究个体。最后，与大多数现实世界的数据一样，这些数据是充满了缺失和不准确。我们建议解决参数估计和模型的问题如此庞大的数据集引发的选择。我们将遵循以下原则：简约和高维优化的例子。大多数具体我们想到的应用涉及成像和基因组学，特别是全基因组关联发现。幸运的是，大多数工具和软件我们构建的产品将更通用。我们成功的算法- 算法将用现代科学编程语言 Julia 进行编码并发布在公开网站上。我们将重点关注受约束的稀疏回归、EM 和 MM 优化算法、方差组件模型、线性混合模型的引导、类联结相关数据模型以及流行病模型中的敏感性分析。这些都是现代基因组学、生物技术中最重要的课题。统计和数据挖掘。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

ORTHOGONAL TRACE-SUM MAXIMIZATION: TIGHTNESS OF THE SEMIDEFINITE RELAXATION AND GUARANTEE OF LOCALLY OPTIMAL SOLUTIONS.

正交迹和最大化：半定松弛的严格性和局部最优解的保证。

DOI：
10.1137/21m1422707
发表时间：
2022
期刊：
SIAM journal on optimization : a publication of the Society for Industrial and Applied Mathematics
影响因子：
0
作者：
Won,Joong-Ho;Zhang,Teng;Zhou,Hua
通讯作者：
Zhou,Hua

MM optimization: Proximal distance algorithms, path following, and trust regions.

DOI：
10.1073/pnas.2303168120
发表时间：
2023-07-04
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
影响因子：
11.1
作者：
Landeros, Alfonso;Xu, Jason;Lange, Kenneth
通讯作者：
Lange, Kenneth

Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo