Automated two-tiered pipeline for exploration of stress response pathways and transcriptomics in chemical risk assessment.
🌐 Live Demo | 📂 Source Code | ✉️ Contact
Welcome to the AOP Project repository! In this repository you will find the hosted website and belonging files. The AOP Project offers:
- The project includes detailed documentation for individual Jupyter Notebooks as well as an available web format that allows for easy navigation.
- Flexibility in generation of enriched AOP networks which enables users to effectively depict complicated biological processes.
- Provision of pathway-centric insights by correlating chemical exposures to adverse outcome pathways (AOPs).
- Openness for data visualisation by enabling users re-usable formats of data that can be inputted for dose-series and time-series analysis and verification of toxicological results.
💡 Built for researchers, data scientists, and regulatory toxicologists interested in AOP-driven analysis.
The AOP Project was created to provide a flexible tool for investigating how inflammatory stress response pathways might be represented in the AOP framework to aid in transcriptomics integration in chemical risk assessment. Chemical risk assessment is a public health concern in which the link to human diseases has been highlighted. This prompted the creation of next-generation risk assessment methodologies such as adverse outcome pathways (AOPs), which focus on inflammatory stress response pathways and make use of transcriptomics. Despite this progress, difficulties persist, which are being addressed by the AOP Project as a critical first step toward standardizing and improving people' health.
⚠️ Make sure Python, JupyterLab and Cytoscape are installed.
git clone https://github.com/ShakiraSA/The-AOP-project.git
cd The-AOP-project
Use Shift + Enter to execute each cell in order.
Feel free to experiment and tailor the code to your own personal research interest.
Version v1.0.0:
