Tumor growth machine learning and understanding
Malignant neoplasms are worldwide one of the leading and increasing causes for death. The underlying complexity of cancer demands for abstractions to disclose an exclusive subset of information related to this disease. Machine learning for tumor growth profiles and model validation may be of great help here. Particularly, discrete multi-agent learning approaches may be of great help. We are dedicated to support cancer research generally, and to reduce in-vivo experiments specifically, following the 3 R’s: Refine > Reduce > Replace. In this project we are working on the design and development of algorithms, methods and tools to support understanding the underlying principles of tumor growth. We follow the assumption that the key to understanding the concepts of cancer lies within an integrative translation and multi-dimensional connection of open data sets. We dedicate our work in memoriam to the family members and friends we have lost. If we may contribute even tiny steps to help to save lives in the future our mission was worth our passion, enthusiasm and effort.