Exploring Multi-Agent Reinforcement Learning for Cell Mechanics
Cell aggregation, where cells stick together, is a key process in many biological events like how embryos form, how tissues heal, and how microbes create communities.Studying this involves looking at different types of data, from detailed molecular information to images and patient data.With new technologies, we have access to large amounts of this data in public databases.Analyzing and combining this complex information requires advanced computer methods.While there are challenges in handling and integrating these diverse datasets, exploring them helps us understand basic biology, develop models for diseases, find new drugs, and advance regenerative medicine.This report reviews these data types, sources, and analysis methods to guide research in this important field.
Authors
- A. Cutolo (ORCID: https://orcid.org/0000-0002-6007-0081)
- Mubarak Shah (ORCID: https://orcid.org/0000-0001-6172-5572)
- Musarat Abbas (ORCID: https://orcid.org/0009-0002-1870-8042)
- Muhammad Waris
Institutions
- Quaid-i-Azam University (PK)
- Federico II University Hospital (IT)
- University of Naples Federico II (IT)
Publication Details
- Journal
- Annals of Computer Science and Information Systems
- Published
- 2025-10-15
- DOI
- https://doi.org/10.15439/2025f4886
- Primary Topic
- Cellular Mechanics and Interactions
- Type
- article
- Field-Weighted Citation Impact
- 0.00