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

项目摘要/摘要 该项目提出了开发新的方法和数据资源来整合现代人工智能(AI) 技术转化为预测毒理学，以及应用这些方法和资源来产生新的假设，将推定的毒物与特定的临床结果联系起来。最近可公开获得的化学和生物医学数据的爆炸性增长为计算毒物学家提供了一个非常有价值的资源，但现有的学习技术 从这些数据中，数据表现很差，未能捕捉到跨越生物组织的多个层次的关键模式。为 例如，美国FDA维护着一个计算毒理学数据库，对超过87.5万种毒理学关注的化学物质进行了编目，但其中只有一小部分已经根据其下游临床效果进行了表征。 然而，信息学和机器学习(ML)提供了可能解决这一问题的特定工具。这个项目的重点是 其中两个特别是：图形机器学习(Graph ML)和语义数据分析。由于这两种技术 允许集成来自多个原本不一致的来源的信息，它们有能力超越 更简单的传统模式发现方法，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 并从现有的公共毒理学数据中验证翻译和机械性结论。在目标1(K99)中，一个新的 数据基础设施-由聚合公共毒理学数据的大型本体控制的图形数据库驱动-将 在计算毒理学中的几个重要任务中被构建和评估。这些资源加在一起，将 被命名为‘CompoxAI’。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 图形数据库中的结果路径(AOPS)。重要的是，这个目标将使用自动机器学习(Auto ML)以数据驱动的方式为该预测任务发现优化的神经网络结构的方法。这 AUTO ML策略将使用分布估计算法(EDAS)来搜索优化的网络结构 以概率的方式。Auto ML方法的一个预期副作用是提高了模型的可解释性 Graph ML的现有应用。目标3(R00)将通过本体推理使用语义数据分析来提炼目标2‘S 模型输出转化为有意义的知识，为新提出的AOPS提出具体的机械解释。 目标4(R00)将利用前几个目标的资源和成果作为起点来开发和传播 新的开源数据标准、软件资源和研究报告协议，目标是创建 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。 在这项工作的方法和基础设施方面的贡献之外，成功完成具体目标 将产生一个将推定的毒物与特定的临床结果联系起来的基于机械的假说的库，解决 这是预测毒理学的一个主要需求。在支持开放科学运动的目标方面，所有研究成果 来自这个项目的--包括论文、软件、数据和其他资源--将免费提供给公众重复使用。

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.