Medicine and HealthNature Communications
Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
H. Gerdes, P. Casado, et al.
Discover the groundbreaking findings of DRUML, a machine learning approach that predicts the efficacy of anti-cancer drugs based on omics data. This research from a team led by Henry Gerdes and others at Barts Cancer Institute reveals remarkable accuracy in drug ranking, paving the way for improved cancer treatment outcomes.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Medicine and Health
Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients
J. Kong, H. Lee, et al.
Chemistry
Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning
A. Daina and V. Zoete
Medicine and Health
Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review
J. Zhang, F. Zhong, et al.
Medicine and Health
Therapeutic efficacy of a MMAE-based anti-DR5 drug conjugate Oba01 in preclinical models of pancreatic cancer
C. Zheng, D. Zhou, et al.
