Disease Modelling


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CBB Member Justin Wan, Computer Science

CBB Member Evelyn Yim, Chemical Engineering

Learn how CBB researchers are investigating Disease Modelling:

CBB Researchers

"Math can predict how cancer cells evolve," , Mohammad Kohandel, January 16, 2018
"Disrupting traditional models of health care," ��ݮ��Ƶ Magazine, Helen Chen, Plinio Morita, Fall 2017
"��ݮ��Ƶ software improves care for kidney patients," ��ݮ��Ƶ Stories, Helen Chen, July 10, 2017
"AI driven imaging research makes diagnosing disease easier," ��ݮ��Ƶ News, Alex Wong, June 7, 2017
"Breakthrough tech helps doctors more accurately diagnose cancer," ��ݮ��Ƶ Stories, Alex Wong, May 20, 2016
"Data management in health care," CBB researcher story, Helen Chen, July 21, 2015
"Improving early diagnosis to save lives," CBB researcher story, Alex Wong, January 9, 2013
"Making it harder for cancer to hide," ��ݮ��Ƶ Stories, Alex Wong, January 9, 2013

"Mathematicians set sights on cancer therapy," , Mohammad Kohandel, January 19, 2018
"Integrative systems for biomedical imaging and analysis," , Alex Wong, October 1, 2014
"Scanning probe microscopy in biomedical research," , Zoya Leonenko, October 1, 2014
"Model-based design in synthetic biology," , Brian Ingalls, September 30, 2014

Zhang, Y., Oikonomou, A., Wong, A., Haider, M. A., & Khalvati, F. (2017). Radiomics-based prognosis analysis for non-small cell lung cancer.��
Shafiee, M. J., Chung, A. G., Khalvati, F., Haider, M. A., & Wong, A. (2017). Discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection.��
Cho, D. S., Khalvati, F., Clausi, D. A., & Wong, A. (2017, July). A Machine Learning-Driven Approach to Computational Physiological Modeling of Skin Cancer.
Kumar, D., Taylor, G. W., & Wong, A. (2017). Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy.��
Jalalimanesh, A., Haghighi, H. S., Ahmadi, A., & Soltani, M. (2017). Simulation-based optimization of radiotherapy: Agent-based modeling and reinforcement learning.��
Meghdadi, N., Niroomand-Oscuii, H., Soltani, M., Ghalichi, F., & Pourgolmohammad, M. (2017). Brain tumor growth simulation: model validation through uncertainty quantification.��
Asgari, H., Soltani, M., & Sefidgar, M. (2018). Modeling of FMISO [F18] nanoparticle PET tracer in normal-cancerous tissue based on real clinical image.��