An Imputation-Consistency Algorithm for Biomedical Complex Data Analysis

项目摘要 在过去的几十年里，数据收集和获取技术的巨大进步使SCI- 从生物医学研究中收集大量与健康相关的数据。如果分析得当，这些数据将 扩展我们的知识，以测试关于疾病管理的新假设，从诊断到预防再到每 声学治疗。然而，生物医学数据可能相当复杂，如何分析它们提出了许多问题 对现有方法的挑战。这项提案试图解决三个基本挑战：(I)失踪 数据在生物医学研究中无处不在，如何在存在的情况下更好地利用生物医学复杂数据 遗漏的值吗？(Ii)随着数据大小的增长，通常会带来 数据和解释这些模式所需的模型。估计参数的一般方法是什么？ 复杂的模型？(Iii)从高通量组学数据中识别生物标记物一直是 癌症研究。然而，尽管进行了激烈的ff，fda每年批准用于临床的生物标记物的数量是 仍然是个位数。造成这一失败的一个重要因素是缺乏适当的统计方法 用于分析这样的异质和高维数据。为了更好地使用生物医学复杂数据， 本课题针对高维缺失数据提出了一种通用的填补一致性算法 有问题。然后，在条件化和条件化的原则下，对算法进行了扩展以解决另外两个挑战 一致性；具体地说，该项目提出了一些高效和高效的统计算法，这些算法解决了以下问题 生物标记物鉴定和eQTL分析中遇到的异质性和高维问题。这个 所提出的算法被应用于(I)利用CCLE和Sanger数据选择抗癌药物敏感基因， (Ii)使用TCGA数据确定多种类型癌症的预后mRNA生物标志物；(Iii)进行eQTL 使用TCGA数据分析多种类型的癌症，以及(Iv)确定信息丰富的循环生物标记物 1型糖尿病。该方法具有较高的ffi效率和通用性，可推广应用于其他类型的疾病。 也是。从统计学上讲，这个项目是为了开发一些通用的、有效的、高效率的复杂算法 数据分析：在生物医学方面，该项目将显著提高从组学角度识别生物标记物的准确性 数据，这增进了人们对分子机制和精准医学发展的理解。 1

Project Summary The dramatic improvement in data collection and acquisition technologies in the past decades has enabled sci- entists to collect vast amounts of health-related data from biomedical studies. If analyzed properly, these data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to per- sonalized treatment. However, the biomedical data can be rather complex, how to analyze them has posed many challenges on the existing methods. This proposal attempts to address three fundamental challenges: (i) Missing data are ubiquitous in biomedical research, how to make a sufficient use of biomedical complex data in presence of missing values? (ii) With the growing data size, typically comes a growing complexity of the patterns in the data and of the models needed to account for the patterns. What is the general recipe for estimating parameters of complex models? (iii) Biomarker identi cation from high-throughput omics data has been one of major focuses in cancer research. Yet despite intense effort, the number of biomarkers approved by FDA each year for clinical use is still in single digits. An important factor contributing to this failure is the lack of appropriate statistical methods for analyzing such heterogeneous and high-dimensional data. Toward a sufficient use of biomedical complex data, this project proposes an imputation-consistency algorithm as a general algorithm for high-dimensional missing data problems. Then the algorithm is extended to address other two challenges under the principles of conditioning and consistency; in particular, this project proposes some highly efficient and effective statistical algorithms that address the heterogeneity and high-dimensionality issues encountered in biomarker identi cations and eQTL analysis. The proposed algorithms are applied to (i) select anticancer drug sensitive genes with the CCLE and SANGER data, (ii) identify prognostic mRNA biomarkers for multiple types of cancers using the TCGA data, (iii) conduct eQTL analysis for multiple types of cancers using the TCGA data, and (iv) identify informative circulating biomarkers for type 1 diabetes. The proposed methods are highly efficient and general and can be applied to other types of disease as well. Statistically, this project is to develop some general, effective, and highly efficient algorithms for complex data analysis; biomedically, this project will signi cantly improve accuracy of biomarker identi cation from omics data, which advances people's understanding of molecular mechanism and development of precision medicine. 1