AOP-helpFinder 3.0: from text mining to network visualization of key event relationships, and knowledge integration...

Area of Science:

• Toxicology
• Computational Biology
• Systems Biology

Background:

• The Adverse Outcome Pathways (AOP) framework standardizes toxicological research by detailing mechanisms from molecular events to adverse outcomes.
• Integrating vast and complex toxicological data remains a significant challenge.

Purpose of the Study:

• To enhance the AOP-helpFinder tool for more effective analysis and integration of toxicological data.
• To improve the identification of stressor-event and event-event relationships for AOP development.

Main Methods:

• Utilized text mining, natural language processing, and graph-based approaches to analyze scientific literature.
• Integrated additional data sources and toxicological database information within a systems biology context.
• Developed an enhanced web server with interactive network visualization capabilities.

Main Results:

• The AOP-helpFinder 3.0 tool automatically screens PubMed literature to identify key relationships for AOP construction.
• New functionalities include annotation with toxicological database information and interactive network visualization.
• Demonstrated the tool's utility through a case study on radon exposure toxicology.

Conclusions:

• AOP-helpFinder 3.0 provides a robust solution for integrative and predictive toxicology.
• The enhanced tool facilitates a more comprehensive understanding of toxicological mechanisms and supports AOP development.