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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要/摘要该项目建议开发新方法和数据资源以整合现代人工智能（AI）预测毒理学的技术以及这些方法和资源的应用来产生将推定毒物与特定临床结果联系起来的新假设。公开可用的化学和生物医学数据的最新爆炸为计算毒理学家提供了极有价值的资源，但是现有的学习技术从这些数据中，表现不佳，无法捕获跨越多个生物组织的关键模式。为了例如，美国FDA保持了一个计算毒理学数据库，该数据库对超过8.75千种化学物质的毒理学问题进行了分类，但仅根据其下游临床效应来表征其中的一小部分。但是，信息和机器学习（ML）提供了可能解决此问题的特定工具。这个项目重点特别是其中的2个：图机学习（图ML）和语义数据分析。由于这两种技术允许从多个原本不一致的来源集成信息，它们具有胜过表现的能力更简单的模式发现方法，同时增加了推论能力和统计能力。我们的中心假设是，语义图数据上的归纳学习提供了一种有效的方法并验证现有公共毒理学数据的翻译和机械结论。在AIM 1（K99）中，新的数据基础架构（由大型，本体控制的图数据库汇总的公共毒理学数据驱动 - 将会在计算毒理学中的几个重要任务中进行构建和评估。这些资源将在一起被命名为“ comptoxai”。 AIM 2（K99）将制定并应用图形机学习策略来预测新的不利图形数据库中的结果途径（AOPS）。重要的是，此目标将使用自动化机器学习（自动） ML）以数据驱动方式发现此预测任务的优化神经网络体系结构的方法。这自动ML策略将使用分销算法（EDA）的估计来搜索优化的网络体系结构以概率的方式。自动ML方法的预期副作用是增加模型的可解释性图ML的现有应用。 AIM 3（R00）将通过本体论推断使用语义数据分析以完善AIM 2 模型输出成有意义的知识，为新提出的AOP提出了特定的机械解释。 AIM 4（R00）将使用以前目标的资源和结果作为开发和传播的起点新的开源数据标准，软件资源和研究报告协议，目的是创建一个计算毒理学AI研究的合作，跨机构研究生态系统。除了这项工作的方法论和基础设施贡献之外，成功完成了具体目标将产生一个基于机械的假设，将推定毒物与特定临床结果联系起来，以解决预测毒理学的主要需求。在支持开放科学运动的目标时，所有研究成果从这个项目（包括论文，软件，数据和其他资源）中，将免费提供公开重复使用。