COPD SUBTYPES AND EARLY PREDICTION USING INTEGRATIVE PROBABILISTIC GRAPHICAL MODELS R01HL157879

使用集成概率图形模型进行 COPD 亚型和早期预测 R01HL157879

基本信息

批准号：
10689580
负责人：
PANAGIOTIS V BENOS
金额：
$ 72.36万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-24 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10689580
关键词：
Address Algorithms Area Asthma Biology Blood Cause of Death Characteristics Chronic Disease Chronic Obstructive Pulmonary Disease Classification Clinical Clinical Data Collaborations Complex Computing Methodologies Data Data Collection Data Set Detection Development Disease Disease Management Disease Progression Disease model Enrollment Ensure Etiology Functional Imaging Future Gene Expression Gene Expression Profile Gene Expression Profiling Genes Genetic Genetic Diseases Genomics Graph Health Care Costs Image Incidence Individual Lead Link Machine Learning Measurement Medicine Methodology Methods Modality Modeling Molecular Molecular Target Multiomic Data Nature Onset of illness Pathway interactions Patients Pattern Phenotype Pulmonary function tests Pulmonology Research Research Personnel Sampling Science Severities Severity of illness Stable Disease Symptoms Syndrome System Testing Time Tissues Training Validation Visit X-Ray Computed Tomography airway obstruction analytical method base cellular targeting chest computed tomography clinical practice clinical subtypes clinically relevant cohort computer framework computerized tools data integration data modeling diagnostic tool disability disease phenotype disorder subtype follow-up genetic variant genomic data imaging genetics improved innovation insight learning algorithm mortality mortality risk multimodal data multimodality multiscale data peripheral blood personalized predictions personalized therapeutic precision medicine predictive modeling prognostic tool prognostic value pulmonary function radiological imaging success treatment guidelines unsupervised learning vector

项目摘要

COPD SUBTYPING AND EARLY PREDICTION USING INTEGRATIVE PROBABILISTIC GRAPHICAL MODELS ABSTRACT One of the main obstacles in developing efficient personalized therapeutic and disease management strategies is that most common diseases are typically defined based on symptoms and clinical measurements, although they are believed to be syndromes, consisting of multiple subtypes with variable etiology. Identifying disease subtypes has thus become very important, but so far it has been met with limited success for most diseases. In asthma, a notable exception, it was the clinical characterization that led to successful subtyping; and this is now incorporated in treatment guidelines. Unsupervised machine learning approaches of single data modalities (e.g., omics, radiographic images) have not produced actionable subtypes due to instability across cohorts. Developing data integrative approaches for multi-scale data, which are becoming available for a number of diseases, is expected to lead to robust subtyping and provide mechanistic insights of disease onset and progression. This proposal focuses on developing new computational methods, based on probabilistic graphical models (PGMs), to address this unmet need; and apply them to investigate three problems of clinical importance in chronic obstructive pulmonary disease (COPD), which is the fourth leading cause of mortality in USA. Our underlying hypothesis is that PGMs can integrate and analyze under the same probabilistic framework heterogeneous biomedical data (omics, chest CT scan, clinical) and identify disease subtypes and their main determinants. The objectives of our proposal is to build a comprehensive computational framework for disease subclassification, identify stable COPD subtypes at the baseline and longitudinally, and build interpretable models of the disease The deliverables of this project are: (1) new integrative computational approaches for clinical subtyping from multi-scale data; (2) new predictors of COPD progression and severity; (3) new discoveries of longitudinally stable COPD subtypes; (4) new predictors of future development of COPD; (5) new omics datasets that will be invaluable to future research in the area (baseline and longitudinal). To ensure the success of the project we follow a team science approach. This multi-PI proposal builds on the ongoing efforts of our group in the area of graphical models and their applications in biomedicine; and the ongoing collaboration of the three PIs that have complementary strengths: Prof. Benos (systems medicine and machine learning), Dr. Hersh (COPD genetics and genomics) and Dr. Sciurba (clinical aspects of COPD). It is powered by the access of the investigators to three major COPD cohorts (COPDGene®, SCCOR, ECLIPSE) that contain multiple parallel deep phenotyping and omics data from thousands of patients and controls. Although in this project we focus on COPD, our methods are generally applicable to any disease, therefore our project will have a positive impact beyond the above deliverables. We believe that due to their robust nature and interpretability, PGMs will soon become the norm for multi-scale biomedical data integration and modeling, when genetic and genomic data collection will become routine prognostic and diagnostic tools in clinical practice.

使用综合概率图进行 COPD 亚型分类和早期预测型号抽象的制定有效的个性化治疗和疾病管理策略的主要障碍之一最常见的疾病通常是根据症状和临床测量来定义的，它们被认为是由具有不同病因的多种亚型组成的综合征。因此，亚型变得非常重要，但迄今为止，它在大多数疾病中取得的成功有限。哮喘是一个值得注意的例外，正是临床特征导致了成功的亚型分型；纳入治疗指南。单一数据模式的无监督机器学习方法（例如，由于队列之间的不稳定，组学、放射图像）尚未产生可操作的亚型。多尺度数据的数据集成方法正在可用于许多疾病预计将导致强大的亚型分析并提供疾病发作和进展的机制见解。该提案侧重于开发基于概率图模型的新计算方法（PGM），解决这一未满足的需求；并应用它们来研究三个具有临床重要性的问题；慢性阻塞性肺疾病（COPD）是美国第四大死亡原因。基本假设是 PGM 可以在相同的概率框架下进行整合和分析异质生物医学数据（组学、胸部 CT 扫描、临床）并识别疾病亚型及其主要亚型我们提案的目标是建立一个全面的疾病计算框架。细分，在基线和纵向上确定稳定的 COPD 亚型，并建立可解释的该项目的可交付成果是：（1）新的综合计算方法来自多尺度数据的临床亚型；(2) COPD 进展和严重程度的新预测因子；(3) 新的预测因子；长期稳定的慢性阻塞性肺病亚型的发现；(4) 慢性阻塞性肺病未来发展的新预测因素；组学数据集对该领域的未来研究（基线和纵向）非常有价值。为了确保该项目的成功，我们遵循团队科学方法，该多 PI 提案建立在我们小组在图形模型及其在生物医学中的应用领域的持续努力；三位具有互补优势的 PI 正在进行合作：Benos 教授（系统医学和机器学习）、Hersh 博士（慢性阻塞性肺病遗传学和基因组学）和 Sciurba 博士（慢性阻塞性肺病的临床方面）。得益于研究人员对三个主要 COPD 队列（COPDGene®、SCCOR、ECLIPSE）的访问，包含来自数千名患者和对照的多个并行深度表型分析和组学数据。这个项目我们专注于慢性阻塞性肺病，我们的方法一般适用于任何疾病，因此我们的项目将我们相信，由于其强大的性质和能力，它们会产生超出上述可交付成果的积极影响。由于可解释性，PGM 将很快成为多尺度生物医学数据集成和建模的规范，当遗传和基因组数据收集将成为临床实践中的常规预后和诊断工具。