Discovering clinical endpoints of toxicity via graph machine learning and semantic data analysis

通过图机器学习和语义数据分析发现毒性的临床终点

• 批准号: 10745593
• 负责人: Joseph Daniel Romano
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10745593
• 关键词: Address; Adoption; Algorithms; Architecture; Artificial Intelligence; Basic Science; Benchmarking; Bioinformatics; Biological; Biological Assay; Cataloging; Chemicals; Clinical; Communities; Complex; Computational Technique; Computer software; Computing Methodologies; Data; Data Analyses; Data Scientist; Databases; Development; Ecosystem; Emerging Technologies; Ensure; Evaluation; Expert Systems; Explosion; Exposure to; Genes; Genetic Programming; Goals; Graph; Human body; Informatics; Institution; Kinesiology; Knowledge; Learning; Libraries; Link; Machine Learning; Mentors; Methodology; Methods; Modeling; Modernization; Names; Ontology; Outcome; Outcomes Research; Output; Paper; Pathway interactions; Pattern; Phase; Process; Productivity; Protocols documentation; Quantitative Structure-Activity Relationship; Reporting; Research; Research Personnel; Resources; Risk; Risk Assessment; Semantics; Signal Transduction; Source; Statistical Models; Structure; Techniques; Technology; Toxic effect; Toxicant exposure; Toxicology; Translation Process; Translational Research; Validation; Work; Xenobiotics; adverse outcome; aggregation database; biomedical data science; clinical effect; clinical predictors; computational toxicology; computing resources; cost; data infrastructure; data resource; data standards; design; diverse data; environmental toxicology; graph database; hands-on learning; improved; informatics tool; innovation; interest; knowledge graph; knowledge translation; learning strategy; method development; multimodal data; multiple omics; network architecture; neural network architecture; new technology; open data; real world application; response; side effect; small molecule; success; tool; toxicant; trend analysis

Project Summary/Abstract This project proposes the development of new methods and data resources to integrate modern artificial intelligence (AI) techniques into predictive toxicology, as well as the application of those methods and resources to generate new hypotheses linking putative toxicants to specific clinical outcomes. The recent explosion of publicly available chemical and biomedical data provides an immensely valuable resource for computational toxicologists, but existing techniques for learning from these data perform poorly and fail to capture crucial patterns that span multiple levels of biological organization. For example, the US FDA maintains a computational toxicology database cataloguing over 875 thousand chemicals of toxicologic concern, yet only a small handful of these have been characterized in terms of their downstream clinical effects. However, informatics and machine learning (ML) provide specific tools that may solve this issue. This project focuses on 2 of those in particular: Graph machine learning (Graph ML) and semantic data analysis. Since both of these techniques allow for the integration of information from multiple otherwise incongruent sources, they have the capacity to outperform simpler traditional methods for pattern discovery, while increasing both inferential capacity and statistical power. Our central hypothesis is that inductive learning on semantic graph data provides an effective means for generating and validating translational and mechanistic conclusions from existing public toxicology data. In Aim 1 (K99), a new data infrastructure—driven by a large, ontology-controlled graph database aggregating public toxicology data—will be constructed and evaluated on several important tasks in computational toxicology. Together, these resources will be named `ComptoxAI'. Aim 2 (K99) will develop and apply a graph machine learning strategy to predict new adverse outcome pathways (AOPs) in the graph database. Importantly, this aim will use an automated machine learning (Auto ML) approach to discover optimized neural network architectures for this prediction task in a data-driven manner. This Auto ML strategy will use estimation of distribution algorithms (EDAs) to search for optimized network architectures in a probabilistic manner. An expected side effect of the Auto ML approach is increased model interpretability over existing applications of Graph ML. Aim 3 (R00) will use semantic data analysis via ontological inference to refine Aim 2's model outputs into meaningful knowledge, proposing specific mechanistic explanations for the newly proposed AOPs. Aim 4 (R00) will use the resources and outcomes of the previous Aims as a starting point to develop and disseminate new open-source data standards, software resources, and research reporting protocols, with the goal of creating a collaborative, cross-institutional research ecosystem for AI research in computational toxicology. Beyond the methodological and infrastructural contributions of this work, successful completion of the Specific Aims will yield a library of mechanistically-based hypotheses linking putative toxicants to specific clinical outcomes, addressing a major need in predictive toxicology. In supporting the goals of the open science movement, all research outcomes from this project—including papers, software, data, and other resources—will be made available for free public reuse.

项目总结/摘要 该项目提出开发新方法和数据资源，以整合现代人工智能（AI） 这些方法和资源的应用，以产生新的假设，将推定的毒物与特定的临床结果联系起来。最近公开的化学和生物医学数据的爆炸为计算毒理学家提供了非常有价值的资源，但现有的学习技术 从这些数据表现不佳，并未能捕捉关键模式，跨越多层次的生物组织。为 例如，美国FDA维护一个计算毒理学数据库，该数据库编目了超过87.5万种毒理学关注的化学品，但其中只有一小部分已经根据其下游临床效应进行了表征。 然而，信息学和机器学习（ML）提供了可以解决这个问题的特定工具。该项目的重点是 其中2个特别的：图机器学习（Graph ML）和语义数据分析。由于这两种技术 允许从多个不一致的来源整合信息，他们有能力超越 更简单的模式发现的传统方法，同时增加了推理能力和统计能力。 我们的中心假设是，对语义图数据的归纳学习提供了一种有效的生成方法。 并验证现有公共毒理学数据的转化和机制结论。在目标1（K99）中， 数据基础设施-由一个大型的，本体控制的图形数据库聚合公共毒理学数据驱动-将 在计算毒理学中的几个重要任务上进行构建和评估。这些资源将 命名为“ComptoxAI”。目标2（K99）将开发和应用图形机器学习策略来预测新的不利因素 图数据库中的结果路径（AOP）。重要的是，这一目标将使用自动机器学习（Auto ML）方法，以数据驱动的方式为该预测任务发现优化的神经网络架构。这 自动ML策略将使用分布估计算法（EDA）来搜索优化的网络架构 以概率的方式。Auto ML方法的一个预期副作用是增加了模型的可解释性， Graph ML的现有应用。目标3（R 00）将通过本体推理使用语义数据分析来细化目标2 将模型输出转化为有意义的知识，为新提出的AOP提出具体的机制解释。 目标4（R 00）将利用先前目标的资源和成果作为起点， 新的开源数据标准、软件资源和研究报告协议，目标是创建一个 协作，跨机构的研究生态系统，用于计算毒理学中的人工智能研究。 除了这项工作在方法和基础设施方面的贡献外， 将产生一个基于机制的假设库，将推定的毒物与特定的临床结果联系起来， 预测毒理学的一个主要需求。为了支持开放科学运动的目标，所有研究成果 从这个项目，包括论文，软件，数据和其他资源，将提供免费的公共再利用。