Planform: an application and database of graph-encoded planarian regenerative experiments.

Area of Science:

• Developmental Biology
• Regenerative Medicine
• Computational Biology

Background:

• Understanding regeneration mechanisms is crucial in biology and medicine.
• Planarian flatworms are key model organisms for regeneration research.
• Existing data lacks computational accessibility, hindering comprehensive modeling.

Purpose of the Study:

• To create a formalized database and software tool for planarian regenerative experiments.
• To enable computational analysis of regeneration data.
• To facilitate the application of artificial intelligence in regeneration research.

Main Methods:

• Developed Planform, a manually curated database based on mathematical graph formalism.
• Integrated over a thousand experiments from key planarian literature.
• Created a software tool with a graphical interface for database interaction.

Main Results:

• Established Planform, a unique database of planarian regenerative experiments.
• The system provides a user-friendly interface for data mining.
• The database is structured for computational understanding, overcoming limitations of textual descriptions.

Conclusions:

• Planform is a valuable resource for the regeneration community.
• The formalized data structure paves the way for AI-driven knowledge extraction.
• This work advances the development of mechanistic models for regeneration.