AOPWIKI-EXPLORER: An interactive graph-based query engine leveraging large language models

IF 3.1 Q2 TOXICOLOGY

Computational Toxicology Pub Date : 2024-03-21 DOI:10.1016/j.comtox.2024.100308

Saurav Kumar , Deepika Deepika , Karin Slater , Vikas Kumar

{"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.

查看原文本刊更多论文

AOPWIKI-ExPLORER：利用大型语言模型的基于图的交互式查询引擎

不良后果途径（AOPs）概述了暴露于应激源后引发的一系列分子和细胞事件，从而为非动物试验提供了依据。近年来，科学界对通过众包方式开发 AOPs 表现出了浓厚的兴趣，并将结果归档到 AOP-Wiki 中：这是一个由经合组织（OECD）协调的集中式资料库，收录了近 512 个 AOPs（2023 年 4 月）。然而，AOP-Wiki 平台目前缺乏多功能查询系统，这阻碍了开发人员对 AOP 网络的探索，也妨碍了其在风险评估中的实际应用。这项工作建议将 AOP-Wiki 的数据改编成标签属性图（LPG）模式，以充分释放 AOP-Wiki 档案的潜力。此外，该工具还为数据库特定查询和自然语言查询提供了一个可视化网络查询界面，从而促进了图数据的检索和分析。多查询界面允许非技术用户构建灵活的查询，从而提高了 AOP 探索的潜力。通过减少时间和技术要求，本查询引擎提高了 AOP-Wiki 中宝贵数据的实际利用率。为了对该平台进行评估，我们介绍了一个案例研究，其中包括三个级别的使用场景（简单、中等和复杂查询）。AOPWIKI-EXPLORER 可在 GitHub (https://github.com/Crispae/AOPWiki_Explorer) 上免费获取，以扩大社区范围并进一步改进。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computational Toxicology Computer Science-Computer Science Applications

CiteScore

5.50

自引率

0.00%

发文量

审稿时长

56 days

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