Cristian Axenie

This paper introduces a machine learning system that discovers mathematical and physical relations in oncology data, revealing human-understandable insights into cancer dynamics and treatment responses.

PERFECTO predicts tumor growth and chemotherapy response using clinical data to improve personalized breast cancer treatment planning.

GLUECK, a neural network model, learns cancer growth curves from clinical datasets without prior knowledge, outperforming traditional tumor growth models.

The paper introduces a Machine Learning model called PRINCESS to predict individual breast cancer evolution to surgical size, showing superior accuracy compared to existing models.

CHIMERA combines mechanistic models and machine learning to optimize chemotherapy and surgery sequencing in breast cancer by learning tumor growth and drug pharmacokinetics.

This paper introduces a machine learning model that learns tumor growth patterns and volume prediction from breast cancer data without supervision, outperforming existing models on clinical datasets.