Research and development of an adverse outcome pathway-focused mechanistic inference tool for 'omics data using semantic knowledge graphs

使用语义知识图研究和开发针对“组学数据”的以不良结果途径为中心的机械推理工具

• 批准号: 10761637
• 负责人: Ruchir Shah
• 金额: $ 24.99万
• 依托单位: SCIOME, LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10761637
• 关键词: 3-Dimensional; Address; Assessment tool; Biochemical; Biochemical Reaction; Biological; Cells; Chemicals; Collection; Controlled Vocabulary; Data; Data Set; Decision Making; Development; Disease; Disparate; Dose; Event; Exposure to; Gene Expression; Generations; Genes; Goals; Health; Individual; Intuition; Knowledge; Language; Link; Machine Learning; Maps; Measurable; Measurement; Methodology; Methods; Modeling; Molecular; Neighborhoods; Online Systems; Ontology; Organ; Organism; Pathway interactions; Population; Process; Proteins; Regulation; Regulatory Pathway; Reproducibility; Research; Research Personnel; Risk Assessment; Semantics; Series; Software Engineering; Source; System; Techniques; Testing; Time; Tissue-Specific Gene Expression; Tissues; Uncertainty; Validation; Visualization; adverse outcome; biomedical ontology; concept mapping; deep learning model; drug development; experience; experimental study; heuristics; improved; information organization; knowledge graph; light weight; novel; protein complex; public database; research and development; response; safety assessment; statistics; stressor; text searching; tool; web app; web interface; web-based tool

Project Summary 1 Adverse outcome pathways (AOPs) are risk assessment tools that provide a transparent, mechanistic description of a 2 stressor resulting in an adverse outcome. Each AOP describes a logical sequence of measurable, causally linked events at 3 varying levels of a biological hierarchy (e.g., molecular, cell, organ, and population). Starting with exposure to a stressor, 4 and proceeding through a series of key events, each AOP terminates with an adverse outcome for the health of an 5 organism or population as a whole. Despite the great popularity of AOPs in risk assessment, there are few tools that can 6 exploit the rich mechanistic information they provide, especially quantitatively and in an automated setting. The lack of a 7 systematic framework for representing AOPs in a way that is amenable to quantitative analysis is an important obstacle 8 that limits their regulatory applications. We have developed a unique system, TOXGRAPH, to address the aforementioned 9 limitations. TOXGRAPH integrates biological and biochemical knowledge across a heterogeneous collection of open 10 biomedical ontologies and public databases: a massive, tissue-specific semantic knowledge graph (KG) curated entirely in- 11 house. We have also developed a deep learning-based model that maps unstructured textual AOP descriptions to specific 12 concepts in this KG. To take advantage of these semantic AOP representations, we have developed a framework that 13 allows us to turn AOPs into hypothesis validation tools, by generating enrichment statistics of AOPs an their individual 14 events. This approach can significantly simplify downstream analysis by calculating enrichment statistics at varying levels 15 of granularity, using the results of experimental datasets, such as the results of differential gene expression experiments 16 to quantify AOP enrichment. 17 The research we propose encompasses three specific aims: (1) improve our AOP enrichment statistics; (2) enhance our 18 NLP mapping of AOP event descriptions to biomedical knowledge graph concepts; (3) software engineering for improved 19 user experience with interactive visualizations. In the first aim, we will improve our model to account for multiple 20 experimental doses and time points concurrently for the enrichment of AOP events. In our second aim, we will improve 21 our semantic similarity model to map arbitrary AOP event descriptions to a controlled vocabulary. For this research, we 22 will employ state-of-the-art machine learning, NLP and text mining methodologies. In our third aim, we will develop a 23 web interface to the current command line tool which besides producing the same enrichment results and mechanistic 24 hypotheses will allow users to navigate the results in a lightweight, interactive 3D space. We envision this as a powerful 25 exploratory tool to generate mechanistic hypotheses, with the added ability to show enriched relations across time 26 points in this 3D space. 27 Our overarching goal is to provide an easy, intuitive, and unbiased (data-driven) framework for querying AOPs and 28 researching mechanism of action. TOXGRAPH will directly contribute towards the development of novel non-animal 29 testing strategies and streamline regulatory decision making. Fundamentally, it will greatly facilitate the intended use of 30 AOPs for regulatory applications: to help minimize the uncertainty in decision making.

项目摘要 1不良后果途径（AOP）是风险评估工具，提供了一个透明的，机械的描述， 2压力源导致不良结果。每个AOP都描述了一系列可测量的、因果关联的事件， 3个不同层次的生物等级（例如，分子、细胞、器官和群体）。从暴露于压力源开始， 4并通过一系列关键事件进行，每个AOP都以一个对 5、整体或整体的生物体。尽管AOP在风险评估中非常流行，但很少有工具可以 6利用它们提供的丰富的机械信息，特别是定量和自动设置。缺乏一个 7以一种易于定量分析的方式表示AOP的系统框架是一个重要的障碍 8、限制其应用范围。我们开发了一个独特的系统TOXGRAPH，以解决上述问题 9限制。TOXGRAPH将生物学和生物化学知识集成在一个开放的异构集合中， 10个生物医学本体和公共数据库：一个庞大的，组织特定的语义知识图（KG），完全在- 11房子我们还开发了一个基于深度学习的模型，该模型将非结构化的文本AOP描述映射到特定的 12个概念在这个KG。为了利用这些语义AOP表示，我们开发了一个框架， 13允许我们通过生成AOP及其个体的富集统计，将AOP转化为假设验证工具。 14个事件。这种方法可以通过计算不同水平的富集统计来显著简化下游分析 15的粒度，使用实验数据集的结果，例如差异基因表达实验的结果 16量化AOP富集。 17我们提出的研究包括三个具体目标：（1）改进我们的AOP富集统计;（2）增强我们的 18 AOP事件描述到生物医学知识图概念的NLP映射;（3）改进的软件工程 19交互式可视化的用户体验。在第一个目标中，我们将改进我们的模型，以考虑多个 20个实验剂量和时间点同时用于AOP事件的富集。在我们的第二个目标中，我们将改进 我们的语义相似性模型将任意AOP事件描述映射到受控词汇表。为了这项研究，我们 22将采用最先进的机器学习，NLP和文本挖掘方法。在我们的第三个目标中，我们将开发一个 23 web界面到当前的命令行工具，除了产生相同的丰富结果和机械 24个假设将允许用户在轻量级的交互式3D空间中导航结果。我们把它想象成一个强大的 25个探索性工具，用于生成机械假设，并具有显示随时间变化的丰富关系的附加功能 在这个3D空间中有26个点。 27我们的首要目标是提供一个简单、直观且无偏见（数据驱动）的框架来查询AOP， 28篇研究机制TOXGRAPH将直接促进新型非动物 29测试策略和简化监管决策。从根本上说，它将大大促进预期的使用， 30个用于监管应用的AOP：帮助最大限度地减少决策过程中的不确定性。