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

基本信息

批准号：
9246538
负责人：
Vanathi Gopalakrishnan
金额：
$ 28.9万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
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/9246538
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 Disease model 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

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 Disease model 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

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项目摘要

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（由适用提供）：科学研究的分子分析数据旨在尽早发现和更好地管理癌症等疾病，这已经以远远超出了我们的能力，无法有效提取对精度医学实践的价值知识。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，从而产生了Panomies数据，例如相同或相关的分类任务，例如基因表达和DNA甲基化。该项目将开发出至关重要的计算方法和工具，以通过相关分子分析研究来改善疾病状态分类的综合建模。该项目将通过以前开发的转移规则学习（TRL）方法（TRL）方法来处理来自生物标志物分析研究的稀疏数据，通过自动从一个数据集中学习分类规则，在从相关数据集中学习规则时传输并使用知识。该项目将开发，应用和评估一种新颖的知识转移方法，该方法涉及使用本体论或分类层次结构以及分类规则学习。具体而言，该项目将检验以下假设：通过本体论结构使用功能映射（TRL-FM）转移分类规则，以提供领域特异性相关性，可以改善Panomies数据对常规方法的整合建模，以产生更好的预测性能并确定更强大的生物标志物针对疾病状态分类。 TRL-FM原型将应用于来自两个潜水区域的现有识别的Panomies数据，用于（1）癌症分类，（2）使用微生物组分析中的全球种群中的寄生虫感染。 TRL-FM模型将使用现有的一组De-sidendified Panomies数据和相关的结节尺寸信息进行验证，以进行精确的肺癌分类和可靠的生物标志物发现，并从一系列高风险的CT CRED患者中进行了相关的大小信息，并与目的的分类器进行了全面比较。该项目可以帮助创建更强大的筛查工具，以确切地分类肺癌，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，有可能使用从粪便微生物组分析获得的数据，有助于改善由蠕虫中最常见的寄生虫造成的筛查和管理，这是影响全球超过十亿人的最常见寄生虫。该项目将产生计算工具，这些工具可以在构建Panomies数据的预测模型时有效地整合来自多个来源的知识。预测模型是高度易于解释的，捕获了数据中亚群的基础的模式，因为分类规则具有有关歧视性生物标志物鲁棒性的增强信息。该项目将通过纳入针对核糖体RNA测序的细菌物种的分析的知识来创造工具，从而使快速增长的人类微生物组研究界受益。 TRL-FM工具将使微生物组数据的集成建模更加有效，从而使对损害或支持人类健康的细菌菌株和物种的快速见解。