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

摘要 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。

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.