Transfer Rule Learning with Functional Mapping for Integrative Modeling of Panomics Data

具有功能映射的转移规则学习用于全景数据的集成建模

• 批准号: 9111473
• 负责人: Vanathi Gopalakrishnan
• 金额: $ 29.6万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-24 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9111473
• 关键词: Affect; Bacteria; Biological; Biological Markers; Cause of Death; Classification; Communicable Diseases; Communities; Computer Simulation; Computing Methodologies; DNA Methylation; Data; Data Set; Detection; Diagnosis; Disease; Disease Management; Early Diagnosis; Fostering; Gene Expression; Genes; Health; Helminths; Human; Human Microbiome; Indonesia; Infection; Ivory Coast; Knowledge; Lead; Learning; Liberia; Link; Lung; Malignant Neoplasms; Malignant neoplasm of lung; Maps; Measures; Medicine; Methods; Modeling; Molecular Profiling; Monitor; Nodule; Ontology; Outcome; Output; Parasites; Parasitic infection; Patients; Pattern; Performance; Phylogenetic Analysis; Population; Probiotics; Psychological Transfer; Research; Ribosomal RNA; Sampling; Source; Structure; Taxonomy; Testing; The Cancer Genome Atlas; Treatment outcome; Trees; United States; Validation; base; biomarker discovery; biomarker panel; cancer biomarkers; cancer classification; case control; cohort; computerized tools; design; disease classification; global health; high risk; high throughput technology; improved; innovation; insight; learning strategy; microbiome; microbiota; novel; precision medicine; predictive modeling; prototype; public health relevance; screening; tool

 DESCRIPTION (provided by applicant): Molecular profiling data from scientific studies aiming for early detection and better management of diseases such as cancer, has accumulated at rates far beyond our abilities to efficiently extract knowledge of value to the practice of precisin medicine. A major challenge is that these data are often generated using multiple high-throughput technologies giving rise to panomics data such as gene expression and DNA methylation for the same or related classification task. This project will develop critically neede computational methods and tools for the integrative modeling of panomics data to improve disease state classification from related molecular profiling studies. This project will extend Transfer Rule Learning (TRL) methods that were previously developed to deal with sparse data from biomarker profiling studies, by automatically learning classification rules from one dataset, transferring that knowledge and using it when learning rules from a related dataset. This project will develop, apply and evaluate a novel method for knowledge transfer that involves the use of ontological or taxonomic hierarchies along with classification rule learning. Specifically, this project will test the hypothesis that transfer learning of classification rules using functional mapping (TRL-FM) via ontological structure to provide domain-specific relatedness improves integrative modeling of panomics data over conventional methods to yield better predictive performance and identify more robust biomarker panels for disease state classification. The TRL-FM prototype will be applied to existing de-identified panomics data from two diverse domains for the classification of (1) cancer, and (2) parasitic infections in global populations using microbiome profiling. The TRL-FM models will be validated for precise lung cancer classification and robust biomarker discovery, using an existing set of de-identified panomics data and related nodule size information from a cohort of high-risk CT-screened patients, and comprehensively compared to state-of-the-art classifiers. This project can help create more robust screening tools for the precise classification of lung cancer, the leading cause of death from cancer in the United States. This project will also impact global health with the potential to help improve screening and management of infections caused by helminths, the most common parasites affecting more than a billion people worldwide, using data obtained from fecal microbiome profiling. This project will result in computational tools that can efficiently integrat knowledge from multiple sources when building predictive models from panomics data. The predictive models are highly interpretable, capturing patterns that underlie subpopulations in the data, as classification rules with augmented information about the robustness of discriminative biomarkers. This project will create tools to benefit the rapidly growing human microbiome research community, by incorporating knowledge specific to the analyses of bacterial species sequenced from ribosomal RNA. The TRL-FM tools will make integrative modeling of microbiome data more efficient thereby enabling rapid insights into bacterial strains and species that harm or support human health.

 描述（由申请人提供）：来自旨在早期检测和更好地管理疾病（如癌症）的科学研究的分子谱数据的积累速度远远超出了我们有效提取对精确医学实践有价值的知识的能力。一个主要的挑战是，这些数据通常是使用多种高通量技术生成的，从而产生泛组学数据，如用于相同或相关分类任务的基因表达和DNA甲基化。该项目将开发尼德计算方法和工具，用于泛组学数据的综合建模，以改善相关分子谱研究的疾病状态分类。该项目将扩展以前开发的用于处理生物标志物分析研究中稀疏数据的传输规则学习（TRL）方法，通过自动学习一个数据集的分类规则，传输该知识并在从相关数据集学习规则时使用它。这个项目将开发，应用和评估一种新的方法，知识转移，涉及使用本体或分类层次沿着分类规则学习。具体而言，该项目将测试以下假设：通过本体结构使用功能映射（TRL-FM）进行分类规则的转移学习，以提供特定领域的相关性，从而改善了泛组学数据的综合建模，从而获得更好的预测性能，并确定更强大的疾病状态分类生物标志物组。TRL-FM原型将应用于来自两个不同领域的现有去识别泛组学数据，用于使用微生物组分析对全球人群中的（1）癌症和（2）寄生虫感染进行分类。TRL-FM模型将被验证用于精确的肺癌分类和稳健的生物标志物发现，使用现有的一组去识别的泛组学数据和来自高风险CT筛查患者队列的相关结节大小信息，并与最先进的分类器进行全面比较。该项目可以帮助创建更强大的筛查工具，用于肺癌的精确分类，肺癌是美国癌症死亡的主要原因。该项目还将影响全球健康， 利用从粪便微生物组分析中获得的数据，帮助改善蠕虫感染的筛查和管理，蠕虫是影响全球10亿多人的最常见寄生虫。该项目将导致计算工具，可以有效地整合从多个来源的知识时，从泛组学数据建立预测模型。预测模型是高度可解释的，捕获数据中亚群的模式，作为具有关于区分性生物标志物的鲁棒性的增强信息的分类规则。该项目将创建工具，通过整合特定于从核糖体RNA测序的细菌物种分析的知识，使快速增长的人类微生物组研究社区受益。TRL-FM工具将使微生物组数据的综合建模更有效，从而能够快速洞察危害或支持人类健康的细菌菌株和物种。