Building a compendium of expert driven read-across cases to facilitate an analysis of the contribution that different similarity contexts play in read-across performance [0.03%]
构建专家驱动的读取案例汇编以分析不同相似性情境对读取性能贡献的影响
Grace Patlewicz,Nathaniel Charest,Amanda Ross et al.
Grace Patlewicz et al.
Read-across is a data-gap filling technique used to predict the toxicity of a target chemical based on data from similar analogues. It is predominantly performed through expert-driven assessments which can limit reproducibility and broader ...
Can graph similarity metrics be helpful for analogue identification as part of a read-across approach? [0.03%]
图相似性度量可否用于类推鉴定以作为跨读方法的一部分？
Brett Hagan,Imran Shah,Grace Patlewicz
Brett Hagan
Read-across is a technique used to fill data gaps for substances lacking specific hazard data. The technique relies on identifying source analogues with relevant data that are 'similar' to the substance of interest (target). Typically, sour...
Development of the toxicity values database, ToxValDB: A curated resource for experimental and derived human health-relevant toxicity data [0.03%]
毒性值数据库ToxValDB的开发：一个用于实验和人类健康相关毒理学数据的整理型资源
Jonathan T Wall,Risa R Sayre,Doris Smith et al.
Jonathan T Wall et al.
The Toxicity Values Database, ToxValDB, was developed by the U.S. EPA Center for Computational Toxicology and Exposure as a resource to curate, store, standardize, and make accessible a wide range of human health-relevant toxicity informati...
The FAIR AOP roadmap for 2025: Advancing findability, accessibility, interoperability, and re-usability of adverse outcome pathways [0.03%]
面向2025的AOP的FAIR路线图：推动不良结局路径的可查找性、可访问性、互操作性和重用性
Holly M Mortensen,Maciej Gromelski,Ginnie Hench et al.
Holly M Mortensen et al.
Adverse Outcome Pathways (AOPs) describe the mechanistic interactions of biological entities with a stressor (chemical, nanomaterial, radiation, virus, etc.) that produce an adverse response. How these interactions and associations are cata...
A comparative study of biostatistical pipelines for benchmark concentration modeling of in vitro screening assays [0.03%]
体外筛选试验基准浓度模型的生物统计学路线图的比较研究
Kelly E Carstens,Arif Dönmez,Jui-Hua Hsieh et al.
Kelly E Carstens et al.
New approach methods (NAMs) have been prioritized to reduce the use of animals for chemical safety assessment while continuing to protect human health and the environment. A key challenge of generating toxicity data is the implementation of...
Development of chemical categories for per- and polyfluoroalkyl substances (PFAS) and the proof-of-concept approach to the identification of potential candidates for tiered toxicological testing and human health assessment [0.03%]
用于识别分层毒理学测试和人类健康评估潜在候选者的全氟和多氟烷基物质(PFAS)化学类别的开发及概念验证方法
G Patlewicz,R S Judson,A J Williams et al.
G Patlewicz et al.
Per- and Polyfluoroalkyl substances (PFAS) are a class of manufactured chemicals that are in widespread use and many present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterized for their ...
Cross-Species Molecular Docking Method to Support Predictions of Species Susceptibility to Chemical Effects [0.03%]
跨物种分子对接方法支持化学物质跨物种敏感性预测
Peter G Schumann,Daniel Chang,Sally Mayasich et al.
Peter G Schumann et al.
NNI Nanoinformatics Conference 2023: Movement Toward a Common Infrastructure for Federal nanoEHS Data Computational Toxicology: Short Communication [0.03%]
纳米信息学会议2023：迈向联邦纳米EHS数据通用基础设施的一步：计算毒理学简报论文
Holly M Mortensen,Jaleesia D Amos,Thomas E Exner et al.
Holly M Mortensen et al.
The National Nanotechnology Initiative organized a Nanoinformatics Conference in the 2023 Biden-Harris Administration's Year of Open Science, which included interested U.S. and EU stakeholders, and preceded the U.S.-EU COR meeting on Novemb...
G Patlewicz,P Karamertzanis,K Paul Friedman et al.
G Patlewicz et al.
Read-across is a well-established data-gap filling technique used within analogue or category approaches. Acceptance remains an issue, mainly due to the difficulties of addressing residual uncertainties associated with a read-across predict...
A Comparison of Machine Learning Approaches for predicting Hepatotoxicity potential using Chemical Structure and Targeted Transcriptomic Data [0.03%]
基于化学结构和目标转录组数据的机器学习预测肝毒性的比较研究
Tia Tate,Grace Patlewicz,Imran Shah
Tia Tate
Animal toxicity testing is time and resource intensive, making it difficult to keep pace with the number of substances requiring assessment. Machine learning (ML) models that use chemical structure information and high-throughput experiment...