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

摘要 生物医学科学正在淹没在大数据中。在以下领域取得的进展 由于基因组学和医学成像正受到缺乏应用程序的阻碍， 适当的计算工具。该补助金促进了 算法，统计方法和软件的分析大 生物医学科学中遇到的数据集。美国国立卫生研究院All of Us Pro- 由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的例子， 最近的大规模数据集。这些数据集需要TB级的存储空间， 样本量范围为105至106名及以上受试者。数据集 也是动态的，随着时间的推移在规模和复杂性上不断增长。此外，本发明还提供了一种方法， 数据集是异构的;例如，英国生物银行提供通用的， 电子健康记录（EHR）数据和成像数据。 相同的研究对象。最后，与大多数真实世界的数据一样， 充满了遗漏和不准确。 我们提出攻击的问题，参数估计和模型 如此庞大的数据集所带来的选择。我们将遵循原则， 简约和高维优化的应用。大部分的具体 我们考虑的应用包括成像和基因组学，特别是 全基因组关联发现幸运的是，大多数工具和软件- 我们构建的软件将更加通用。我们成功的算法- 算法将使用现代科学编程语言Julia 并发布在公开网站上。我们将专注于受约束的 和稀疏回归，EM和MM优化算法，方差 成分模型，线性混合模型的自举，copula类 相关数据模型和流行病模型的敏感性分析。 这些都是现代基因组学、生物学和生物医学中最重要的课题。 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.