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Interpretable graphical models for large multi-modal COPD data (R01HL159805)

大型多模态 COPD 数据的可解释图形模型 (R01HL159805)

基本信息

批准号：
10705824
负责人：
PANAGIOTIS V BENOS
金额：
$ 47.25万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-25 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10705824
关键词：
Address Affect Algorithms Area Biological Markers Cancer Patient Cause of Death Chronic Obstructive Pulmonary Disease Chronic lung disease Classification Clinical Clinical Data Combined Modality Therapy Complex Computer software Data Data Analyses Data Collection Data Set Diagnostic Procedure Disease Disease Progression Evaluation Explosion Face Genetic Genomics Grant Graph Health Care Costs Hospitals Image Internet Joints Knowledge Learning Letters Libraries Lung nodule Machine Learning Malignant Neoplasms Malignant neoplasm of lung Medical Medicine Methodology Methods Modality Modeling Outcome Pathogenesis Patients Pneumonia Probability Process Production Property Pythons Research Research Personnel Risk Factors Sampling Series System Theoretical model Time Training Validation X-Ray Computed Tomography biomarker selection clinical development clinically relevant cohort complex data data streams deep learning disability diverse data flexibility graph learning high dimensionality low dose computed tomography machine learning method microbiome modifiable risk mortality multimodal data multimodality novel personalized medicine precision medicine predictive modeling programs radiological imaging random forest screening success theories tool web portal web server

项目摘要

INTERPRETABLE GRAPHICAL MODELS FOR LARGE MULTI-MODAL COPD DATA ABSTRACT One of the most important tasks in today’s era of precision medicine is to understand the mechanisms and the factors affecting the development of clinical outcomes. Another important task is to develop interpretable, predictive models for outcomes. In the last years, many machine learning methods have dominated the task of predictive modeling, including deep learning, random forests and others. They are fueled by the unprecedent volume of data that have been generated in some research areas. However, the interpretability of these methods is not straight forward and their accuracy decreases when only small to medium size training datasets are available. Furthermore, their predictive models do not uncover the complex web of interactions between other variables in the dataset, which is essential for fully understanding disease mechanisms. Also, most such methods are not well suited to accommodate mixed data types (e.g., continuous, discrete) in the same dataset. Probabilistic graphical models (PGMs) offer a promising alternative to classical machine learning methods, because they are flexible and versatile. They can identify both the direct (causal) relations between variables, pointing to disease mechanisms, and build predictive models over diverse data, with good results even with smaller training datasets. They have been used for classification, biomarker selection, identification of modifiable risk factors of an outcome, or for mechanistic studies of perturbations of disease networks. In the previous years we extended the PGM theoretical framework to the analysis of mixed continuous and discrete variables, with or without unmeasured confounders; and we can now evaluate and incorporate prior information in mixed data graph learning. We successfully applied those methods to diverse clinically important problems, including malignancy prediction of undetermined lung nodules, identification of microbiome and other factors affecting pneumonia, selection of SNP biomarkers for combination treatment of cancer patients. Our objective is to develop novel interpretable methods for analysis of any-type data and use them to address clinically relevant questions in COPD, an important chronic lung disease. Method evaluation will be done on synthetic and real data, including parallel datasets with genomic, genetic, imaging and clinical COPD data. Our central aim is to identify factors of disease mechanisms of progression using different modalities of patient data. The deliverables will be (1) new PGM approaches for integrative analysis of any-type data; (2) a new, fully documented software package (in R, Python) that can be incorporated in other pipelines; (3) a new web portal to disseminate our methodologies to non-computer-savvy COPD researchers; (4) results on the pathogenesis and predictive features of chronic obstructive pulmonary disease (COPD). This cross-disciplinary team project is expected to have a positive impact beyond the above deliverables, since the generality of our approaches makes them suitable for studying any disease; and they can be easily integrated into personalized medicine strategies when high-throughput data collection will become a routine diagnostic procedure in all hospitals.

大型多模态COPD数据的可解释图形模型摘要当今精准医学时代最重要的任务之一是了解其机制和影响临床结果发展的因素。另一个重要的任务是开发可解释的，结果的预测模型。在过去的几年里，许多机器学习方法已经主导了预测建模，包括深度学习、随机森林等。他们被前所未有的在某些研究领域产生的大量数据。然而，这些方法的可解释性不是直接的，当只有小到中等大小的训练数据集时，它们的准确性会降低。 available.此外，他们的预测模型并没有揭示其他人之间复杂的相互作用网络。数据集中的变量，这对于充分理解疾病机制至关重要。此外，大多数此类方法不太适合于容纳混合数据类型（例如，连续的，离散的）在同一个数据集中。概率图模型（PGMs）为经典机器学习方法提供了一种有前途的替代方法，因为它们是灵活和通用的。它们可以识别变量之间的直接（因果）关系，指出疾病机制，并在不同的数据上建立预测模型，即使在较小的训练数据集。它们已被用于分类，生物标志物的选择，结果的风险因素，或疾病网络扰动的机制研究。前几年我们将PGM理论框架扩展到连续和离散混合变量的分析，没有不可测量的混杂因素;我们现在可以评估和合并混合数据中的先验信息图形学习。我们成功地将这些方法应用于各种临床重要问题，包括未确定的肺结节的恶性预测，微生物组的鉴定和其他影响因素肺炎，选择SNP生物标志物用于癌症患者的联合治疗。我们的目标是开发新的可解释的方法来分析任何类型的数据，并使用它们来解决 COPD是一种重要的慢性肺部疾病。方法评价将在合成和真实的数据，包括具有基因组、遗传、成像和临床COPD数据的并行数据集。我们中心目标是使用不同形式的患者数据来识别疾病进展机制的因素。可交付成果将是（1）用于任何类型数据综合分析的新PGM方法;（2）一种新的、完全的文档化的软件包（R，Python），可以合并到其他管道中;（3）新的门户网站向不熟悉计算机的COPD研究人员传播我们的方法;（4）关于发病机制的结果和慢性阻塞性肺疾病（COPD）的预测特征。这个跨学科的团队项目预计将产生超出上述交付成果的积极影响，因为我们的方法具有普遍性使它们适合研究任何疾病;它们可以很容易地集成到个性化医疗中高通量数据收集将成为所有医院的常规诊断程序。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

FEV1/FVC Severity Stages for Chronic Obstructive Pulmonary Disease.

慢性阻塞性肺疾病的 FEV1/FVC 严重程度阶段。

DOI：
10.1164/rccm.202303-0450oc
发表时间：
2023
期刊：
American journal of respiratory and critical care medicine
影响因子：
24.7
作者：
Bhatt,SuryaP;Nakhmani,Arie;Fortis,Spyridon;Strand,MatthewJ;Silverman,EdwinK;Sciurba,FrankC;Bodduluri,Sandeep
通讯作者：
Bodduluri,Sandeep

Integrated unbiased multiomics defines disease-independent placental clusters in common obstetrical syndromes.

DOI：
10.1186/s12916-023-03054-8
发表时间：
2023-09-08
期刊：
BMC MEDICINE
影响因子：
9.3
作者：
Barak, Oren;Lovelace, Tyler;Piekos, Samantha;Chu, Tianjiao;Cao, Zhishen;Sadovsky, Elena;Mouillet, Jean-Francois;Ouyang, Yingshi;Parks, W. Tony;Hood, Leroy;Price, Nathan D.;Benos, Panayiotis V.;Sadovsky, Yoel
通讯作者：
Sadovsky, Yoel

Constructing Causal Life-Course Models: Comparative Study of Data-Driven and Theory-Driven Approaches.

构建因果生命历程模型：数据驱动和理论驱动方法的比较研究。