Causal graphical methods for high-dimensional heterogeneous biomedical data

高维异构生物医学数据的因果图方法

• 批准号: 10625257
• 负责人: Tyler Lovelace
• 金额: $ 4.77万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-21 至 2025-03-20
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10625257
• 关键词: Address; Algorithms; Automobile Driving; Biological; Cells; Clinical; Complex; Data; Data Analytics; Data Set; Development; Dimensions; Event; Explosion; Generations; Genes; Graph; Heterogeneity; Immunologics; Individual; Intensive Care Units; Intervention; Intubation; Investigation; Learning; Life; Malignant Neoplasms; Measures; Medical; Medicine; Methodology; Methods; Mining; Modeling; Mortality Determinants; Motivation; Outcome; Patients; Performance; Periodicity; Phenotype; Process; Property; RNA; Research; Research Personnel; Resolution; Skeleton; Structure; System; Testing; The Cancer Genome Atlas; Time; Validation; Ventilator; Work; analytical method; cancer care; causal model; cell type; clinically relevant; cohort; complex data; coping; experimental study; flexibility; gene regulatory network; graph learning; high dimensionality; improved; learning algorithm; learning strategy; machine learning method; malignant breast neoplasm; method development; model design; mortality; multidimensional data; multiple omics; novel; predictive modeling; prognostic model; single-cell RNA sequencing; tool; vector

In the past decade, there has been an explosion of data collected from biological and biomedical systems, both in terms of type and volume. Mining these high-dimensional, heterogeneous, and often dynamic datasets to make biologically or medically important inferences or develop predictive models requires new sophisticated data analytics methods. New machine learning methods have begun filling this gap, but most of these methods generate “black box” models that lack clear interpretability. Additionally, these methods are associative, and are thus incapable of teasing out the complex cause-effect relationships among features in the dataset. Directed causal graphical models (DCGMs) are a powerful tool for filling this gap. DCGMs, learned from observational datasets, can represent causal relationships between variables. This allows DCGMs to generate hypotheses of mechanisms and construct parsimonious, causally informed predictive models. However, biomedical datasets often have features that make it difficult to construct causal graphical models over the full dataset. Examples include: data type heterogeneity, high dimensionality, multicollinearity, cyclicity, and nonstationarity. To address these problems, I propose to develop methods for learning causal graphs in datasets containing (1) a heterogeneous mixture of continuous, categorical, and censored variables, (2) high dimensionality and multicollinearity, and (3) cyclicity and nonstationarity. In Aim 1, I will develop a new causal discovery algorithm that accommodates continuous, categorical and censored variables (e.g., survival). In Aim 2, I will test and compare various methods for matrix decomposition and dimensionality reduction in their ability to learn a meaningful low-dimensional latent feature space to be used in graph learning methods. In Aim 3, I will develop a new method for causal discovery in dynamic, possibly cyclic, gene regulatory networks at single cell resolution. In all cases, testing and validation will be performed on synthetic and real-life publicly available datasets. These methodological improvements constitute important steps forward in the field of causal discovery and they can be utilized together or independently to provide a flexible and powerful platform for analysis of a wide range of biomedical datasets. Once made available, they will enable researchers to make inferences about causal mechanisms, generate hypotheses, and build robust, parsimonious predictive models.

在过去的十年中，这是从生物和生物医学系统中收集的数据的爆炸 在类型和音量方面。 进行生物学或医学上重要的推论或开发预测模型要求新的复杂性 数据分析方法。 生成缺乏明确解释的“黑匣子”模型。 因此，无法逗弄数据集中特征之间复杂的因果关系。 因果图形模型（DCGM）是从观察值中学到的强大工具。 数据集，可以压制变量之间的因果关系。 机制和构建了典型的因果信息预测模型。 通常具有使构建因果图形模型在完整示例上构建的特征 包括：数据类型异质性，高贵族性，多重共线性，周期性和非定理性。 这些问题，我建议开发在数据集中学习因果图的方法（1）A 连续，分类和人口普查变量的异质混合物，（2）高率和高率 多连接性，（3）在AIM 1中，我将开发出一种新的Discovery算法 这可容纳连续的，分类和审查的变量（例如，在AIM 2中，我将测试和测试 比较矩阵分解和维度修复的各种方法的学习能力 在AIM 3中使用的有意义的低维潜在功能。 单个CELLE分辨率的动态，可能的循环，基因调节网络中因果发现的新方法。 在所有情况下，都将对可用的合成和真实公共生活进行测试和验证。 方法论改进构成了因果发现领域的重要步骤，它们可以 一起使用或独立利用，以提供一个灵活而强大的平台，以分析广泛的范围 生物医学数据集。 机制，产生假设并建立强大的，简约的预测模型。