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: 10371656
• 负责人: Joseph Daniel Romano
• 金额: $ 9.15万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10371656
• 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; Foundations; Genes; Genetic Programming; Goals; Government; Graph; Human body; Informatics; Infrastructure; 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; Translational Research; Validation; Work; Xenobiotics; adverse outcome; base; biomedical data science; clinical effect; clinical predictors; computational toxicology; computing resources; cost; data infrastructure; data resource; data standards; design; diverse data; environmental toxicology; hands-on learning; improved; informatics tool; innovation; interest; knowledge graph; 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/文摘