Recommender system for cancer treatment

Thesis Type Master
Thesis Status
Currently running
Student Balthasar Huber
Thesis Supervisor
External Supervisor
Zlatko Trajanoski

Recommender systems are used to increase user satisfaction on specific platforms by recommending suitable items (products, movies, songs) to a user. In the context of biology and medicine however, recommender systems remain widely untouched, although machine learning algorithms for drug combination or treatment outcome prediction are very popular. n this thesis, we aim to show that it is possible to use the strengths (e.g., personalized predictions) and typical characteristics (e.g., ranking of items) of recommender systems to successfully predict and suggest drugs to cancer patients.