{"title":"AOPWIKI-EXPLORER: An interactive graph-based query engine leveraging large language models","authors":"Saurav Kumar , Deepika Deepika , Karin Slater , Vikas Kumar","doi":"10.1016/j.comtox.2024.100308","DOIUrl":null,"url":null,"abstract":"<div><p>Adverse Outcome Pathways (AOPs) provide a basis for non-animal testing, by outlining the cascade of molecular and cellular events initiated upon stressor exposure, leading to adverse effects. In recent years, the scientific community has shown interest in developing AOPs through crowdsourcing, with the results archived in the AOP-Wiki: a centralized repository coordinated by the OECD, hosting nearly 512 AOPs (April, 2023). However, the AOP-Wiki platform currently lacks a versatile querying system, which hinders developers' exploration of the AOP network and impedes its practical use in risk assessment. This work proposes to unleash the full potential of the AOP-Wiki archive by adapting its data into a Labelled Property Graph (LPG) schema. Additionally, the tool offers a visual network query interface for both database-specific and natural language queries, facilitating the retrieval and analysis of graph data. The multi-query interface allows non-technical users to construct flexible queries, thereby enhancing the potential for AOP exploration. By reducing the time and technical requirements, the present query engine enhances the practical utilization of the valuable data within AOP-Wiki. To evaluate the platform, a case study is presented with three levels of use-case scenarios (simple, moderate, and complex queries). AOPWIKI-EXPLORER is freely available on GitHub (https://github.com/Crispae/AOPWiki_Explorer) for wider community reach and further enhancement.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111324000100/pdfft?md5=542059b7f2c1ba3e8e43c9fa101d3325&pid=1-s2.0-S2468111324000100-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111324000100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Adverse Outcome Pathways (AOPs) provide a basis for non-animal testing, by outlining the cascade of molecular and cellular events initiated upon stressor exposure, leading to adverse effects. In recent years, the scientific community has shown interest in developing AOPs through crowdsourcing, with the results archived in the AOP-Wiki: a centralized repository coordinated by the OECD, hosting nearly 512 AOPs (April, 2023). However, the AOP-Wiki platform currently lacks a versatile querying system, which hinders developers' exploration of the AOP network and impedes its practical use in risk assessment. This work proposes to unleash the full potential of the AOP-Wiki archive by adapting its data into a Labelled Property Graph (LPG) schema. Additionally, the tool offers a visual network query interface for both database-specific and natural language queries, facilitating the retrieval and analysis of graph data. The multi-query interface allows non-technical users to construct flexible queries, thereby enhancing the potential for AOP exploration. By reducing the time and technical requirements, the present query engine enhances the practical utilization of the valuable data within AOP-Wiki. To evaluate the platform, a case study is presented with three levels of use-case scenarios (simple, moderate, and complex queries). AOPWIKI-EXPLORER is freely available on GitHub (https://github.com/Crispae/AOPWiki_Explorer) for wider community reach and further enhancement.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs