Investigation of Expert Rule Bases, Logistic Regression, and Non-linear Machine 
Learning Techniques for Predicting Response to Antiretroviral Treatment

Prosperi, Mattia C. F.; Altmann, André; Rosen-Zvi, Michal; Aharoni, Ehud; Gabor, Borgulya; Fulop, Bazso; Sonnerborg, Anders; Schulter, Eugen; Struck, Daniel; Ulivi, Giovanni; Vandamme, Anne-Mieke; Vercauteren, Jurgen; Zazzi, Maurizio

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Investigation of Expert Rule Bases, Logistic Regression, and Non-linear Machine Learning Techniques for Predicting Response to Antiretroviral Treatment

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Altmann, André
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Prosperi, M. C. F., Altmann, A., Rosen-Zvi, M., Aharoni, E., Gabor, B., Fulop, B., et al. (2009). Investigation of Expert Rule Bases, Logistic Regression, and Non-linear Machine Learning Techniques for Predicting Response to Antiretroviral Treatment. Antiviral Therapy, 14(3), 433-442. Retrieved from https://www.intmedpress.com/journals/avt/abstract.cfm?id=72&pid=88.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0019-FA28-C

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