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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.

Copd亚型分型及综合概率图早期预测