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: 10388447
• 负责人: Tyler Lovelace
• 金额: $ 4.68万
• 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/10388447
• 关键词: Address; Algorithms; Automobile Driving; Biological; Categories; Cells; Clinical; Complex; Data; Data Analytics; Data Set; Development; Dimensions; Event; Explosion; Generations; Genes; Graph; Heterogeneity; Immunologics; Individual; Intensive Care Units; Intervention; 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; design; experimental study; flexibility; gene regulatory network; graph learning; high dimensionality; learning algorithm; learning strategy; machine learning method; malignant breast neoplasm; method development; mortality; 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，从生物和生物医学系统收集的数据激增 挖掘这些高维、异构且通常是动态的数据集 做出生物学或医学上重要的推论或开发预测模型需要新的复杂技术 新的机器学习方法已经开始填补这一空白，但其中大多数方法。 此外，这些方法是关联的，并且是缺乏明确可解释性的“黑盒”模型。 因此无法梳理出 Directed 数据集中特征之间复杂的因果关系。 因果图模型（DCGM）是从观察中学到的填补这一空白的强大工具。 数据集，可以表示变量之间的因果关系，这使得 DCGM 能够生成假设。 然而，生物医学数据集。 通常具有难以在完整数据集示例上构建因果图形模型的功能。 包括：数据类型异质性、高维性、多重共线性、周期性和非平稳性。 这些问题，我建议开发在包含（1）的数据集中学习因果图的方法 连续变量、分类变量和删失变量的异质混合，(2) 高维和 多重共线性，以及 (3) 周期性和非平稳性 在目标 1 中，我将开发一种新的因果发现算法。 容纳连续变量、分类变量和审查变量（例如，生存）。在目标 2 中，我将测试和 比较各种矩阵分解和降维方法的学习能力 在目标 3 中，我将开发用于图学习方法的有意义的低维潜在特征空间。 一种在单细胞分辨率下动态、可能循环的基因调控网络中因果发现的新方法。 在所有情况下，测试和验证都将在合成的和现实的公开数据集上进行。 方法论的改进是因果发现领域向前迈出的重要一步，它们可以 可以一起或独立使用，为各种分析提供灵活而强大的平台 一旦提供生物医学数据集，它们将使研究人员能够对因果关系做出推断。 机制、生成假设并建立稳健、简约的预测模型。