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)

• 批准号: 10689574
• 负责人: PANAGIOTIS V BENOS
• 金额: $ 50.18万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 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/10689574
• 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; Dose; 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; biomarker selection; cancer therapy; clinical development; clinically relevant; cohort; data streams; deep learning; disability; diverse data; flexibility; graph learning; high dimensionality; machine learning method; microbiome; modifiable risk; mortality; multimodal data; multimodality; non-Gaussian model; novel; personalized medicine; precision medicine; predictive modeling; programs; radiological imaging; random forest; success; theories; tool; web portal; web server

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.

用于大型多模式COPD数据的可解释图形模型 摘要 在当今精准医学时代，最重要的任务之一是了解 影响临床结果发展的因素。另一项重要任务是开发可解释的、 对结果的预测模型。在过去的几年里，许多机器学习方法已经主导了任务 预测建模，包括深度学习、随机森林等。他们被史无前例的 在某些研究领域产生的数据量。然而，这些方法的可解释性 不是直截了当的，当只有中小型训练数据集时，它们的精度会降低 可用。此外，他们的预测模型没有揭示彼此之间相互作用的复杂网络 数据集中的变量，这对于充分了解疾病机制是必不可少的。此外，大多数这样的方法 不适合在同一数据集中容纳混合数据类型(例如，连续、离散)。 概率图形模型(PGMS)为传统机器学习方法提供了一种很有前途的替代方法， 因为它们既灵活又多才多艺。它们可以识别变量之间的直接(因果)关系， 指向疾病机制，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、生物标记物选择、可修饰的 结果的风险因素，或疾病网络扰动的机制研究。在前几年 我们将PGM理论框架扩展到混合的连续变量和离散变量的分析，其中或 没有不可测量的混杂因素；我们现在可以在混合数据中评估和合并先验信息 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 未确定肺结节的恶性预测、微生物群鉴定及其他影响因素 肺炎，选择SNP生物标记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 慢性阻塞性肺疾病，一种重要的慢性肺部疾病的临床相关问题。方法评估将在以下方面进行 合成和真实数据，包括与基因组、遗传、成像和临床COPD数据并行的数据集。我们的 中心目标是使用不同形式的患者数据来识别疾病进展的因素和机制。 交付成果将是(1)用于任何类型数据的综合分析的新的PGM方法；(2)新的、完整的 可合并到其他管道中的文档化软件包(R，Python)；(3)新的Web门户 向不精通计算机的COPD研究人员传播我们的方法；(4)关于发病机制的结果 慢性阻塞性肺疾病(COPD)的预测特征。这个跨学科的团队项目 预计将产生上述交付成果之外的积极影响，因为我们的方法具有普遍性 使它们适合于研究任何疾病；并且它们可以很容易地集成到个性化医学中 当高通量数据收集将成为所有医院的常规诊断程序时的战略。