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：
10705838
负责人：
PANAGIOTIS V BENOS
金额：
$ 70.53万
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/10705838
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 Future Gene Expression Gene Expression Profile Gene Expression Profiling Genes Genetic Genetic Diseases Genomics Graph Health Care Costs Image Incidence Individual Link Machine Learning Measurement Medicine Methodology Methods Modality Modeling Molecular Molecular Target Multiomic Data Nature Onset of illness Pathway interactions Patients Pattern Phenotype Physiological 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 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 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.

COPD亚型和早期预测使用综合概率图形图型号抽象的开发有效的个性化疗法和疾病管理策略的主要障碍之一是否通常根据症状和临床测量来定义最常见的疾病，尽管它们被认为是综合症，由多种子类型组成，具有可变的病因。识别疾病因此，亚型已经变得非常重要，但是到目前为止，大多数疾病的成功都有限。在哮喘是一个值得注意的例外，正是临床表征导致了成功的亚型。现在是纳入治疗指南。无监督的单个数据模式的机器学习方法（例如， OMICS，放射线图）由于整个队列的不稳定性而没有产生可操作的亚型。发展用于多种疾病的多尺度数据的数据集成方法是预计会导致强大的亚型，并提供疾病发作和进展的机械见解。该建议重点是基于概率图形模型开发新的计算方法（PGMS），以满足这种未满足的需求；并应用它们调查临床重要性的三个问题慢性阻塞性肺疾病（COPD），这是美国死亡率的第四个主要原因。我们的基本假设是PGM可以在相同的概率框架下整合和分析异质生物医学数据（OMICS，胸部CT扫描，临床）并鉴定疾病亚型及其主要确定。我们提案的目标是建立一个疾病的综合计算框架子分类，在基线和纵向上识别稳定的COPD亚型，并构建可解释的疾病模型该项目的可交付成果是：（1）来自多尺度数据的临床亚型；（2）COPD进展和严重性的新预测指标；（3）新纵向稳定的COPD亚型的发现；（4）COPD未来发展的新预测指标；（5）新对该地区未来研究（基线和纵向）将是无价的OMIC数据集。为了确保项目的成功，我们遵循团队科学方法。这个多PI的建议建立在我们小组在图形模型领域的持续努力及其在生物医学中的应用；和具有互补优势的三个PI的持续合作：Benos教授（系统医学和机器学习），HERSH博士（COPD遗传学和基因组学）和Sciurba博士（COPD的临床方面）。这是通过调查人员进入三个主要的COPD队列（COPDGENE®，SCCOR，ECLIPSE）的动力包含来自数千名患者和对照组的多个平行深层表型和OMIC数据。虽然在我们专注于COPD的项目，我们的方法通常适用于任何疾病，因此我们的项目将在上述可交付成果之外产生积极影响。我们相信，由于它们的强大性质和可解释性，PGM很快将成为多尺度生物医学数据集成和建模的规范遗传和基因组数据收集将成为临床实践中常规的预后和诊断工具。