This article introduces StreamChol, a software for developing and applying mechanistic models to predict cholestasis. StreamChol is a Streamlit application, usable as a desktop application or web-accessible software when installed on a server using a docker container. StreamChol allows a seamless integration of pharmacokinetic analyses with Machine Learning models. This integration not only enables cholestasis prediction but also opens avenues for predicting other toxicological endpoints requiring similar integrations. StreamChol's Docker containerization also streamlines deployment across diverse environments, addressing potential compatibility issues. StreamChol is distributed as open-source under GNU GPL v3, reflecting our commitment to open science. Through StreamChol, researchers are offered a potent tool for predictive modelling in toxicology, harnessing its strengths within an intuitive and user-friendly interface, without the need for any programming knowledge. Scientific contribution This work offers a user-friendly web-based tool for cholestasis prediction and a complete workflow for creating web platforms that require the combination of both programming languages, R and Python.

中文翻译：

---

StreamChol：用于预测胆汁淤积的基于 Web 的应用程序


本文介绍了 StreamChol，这是一款用于开发和应用机制模型来预测胆汁淤积的软件。StreamChol 是一个 Streamlit 应用程序，当使用 docker 容器安装在服务器上时，可用作桌面应用程序或 Web 可访问软件。StreamChol 允许将药代动力学分析与机器学习模型无缝集成。这种集成不仅实现了胆汁淤积预测，还为预测其他需要类似集成的毒理学终点开辟了途径。StreamChol 的 Docker 容器化还简化了跨不同环境的部署，解决了潜在的兼容性问题。StreamChol 在 GNU GPL v3 下作为开源分发，反映了我们对开放科学的承诺。通过 StreamChol，研究人员获得了毒理学预测建模的有力工具，在直观且用户友好的界面中利用其优势，无需任何编程知识。科学贡献这项工作提供了一个用户友好的基于 Web 的工具，用于胆汁淤积预测，以及用于创建需要结合编程语言 R 和 Python 的 Web 平台的完整工作流程。