Dr. Axenie was invited to edit a new special issue in the prestigious MDPI Symmetry Journal on "Networks in Cancer: From Symmetry Breaking to Targeted Therapy".
Context Chemotherapy-induced neuropathies (CIPN) have gained clinical significance due to the prevalence of malignant disease and the use of new chemotherapeutic drugs; their prevalence is also reported to be
On Thursday, the 8th of April 2021, 10:00, AKII Lab hosts a seminar on Biomechanics and Rehabilitation with a great invited talk from a world expert in Robotic
Dr. Axenie was invited to hold a talk at the Integrative Cancer Biology Lab (Markowetz Lab), Cancer Research UK Cambridge Institute, the University of Cambridge under the Cancer
Dr. Axenie was invited to hold a lecture on Mathematical and Computational Oncology in the PULMOSESNS Lecture Series at Universität Ulm (https://www.uni-ulm.de/en/in/pulmosens/qualification-and-equality/lecture-series/). The AKII Lab research local visibility
Our new paper combining machine learning and mechanistic modelling for predicting chemotherapy outcome entitled "PERFECTO: Prediction of Extended Response and Growth Functions for Estimating Chemotherapy Outcomes in Breast Cancer"
Our latest paper CHIMERA: Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer by Cristian Axenie and Daria Kurz, received the "Springer Best
Our new paper combining machine learning and mechanistic modelling for breast cancer surgery-chemotherapy sequencing entitled „CHIMERA: Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in
Our latest manuscript on the Role of Kinematics Assessment and Multimodal Sensorimotor Training for Motion Deficits in Breast Cancer Chemotherapy-Induced Polyneuropathy: A Perspective on Virtual Reality Avatars (by
Our latest paper, on learning mathematical relations among tumor histopathological and morphological parameters to capture phenotypical growth transitions, entitled "Tumor Characterization using Unsupervised Learning of Mathematical Relations within Breast