Selecting anti-HIV therapies based on a variety of genomic and clinical factors
By Michal Rosen-Zvi, André Altmann, Mattia C. F. Prosperi, Ehud Aharoni, Hani Neuvirth, Anders Sönnerborg, Eugen Schülter, Daniel Struck, Y. Peres, F. Incardona and 3 more. 2008
75 citations recorded by the source corpus.
Motivation: Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy. Results: Three different machine learning techniques were used: generative–discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome. Availability: The combined prediction engine will be available from July 2008, see http://engine.euresist.org Contact: rosen@il.ibm.com
Subfields: Genetics, Infectious Diseases, Virology
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Citations in the corpus
- Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation (2018, background): “[30] proposed to utilize genomic information for recommending suitable treatments for patients with different diseases.”
- Towards Personalized Medicine: Leveraging Patient Similarity and Drug Similarity Analytics (2014, background): “There are also some studies 5, 6 of combining clinical and genetics/genomics information in selecting optimal clinical treatments.”
- Multi-task learning for HIV therapy screening (2008, methodology): “We use data from the EuResist project (Rosen-Zvi et al., 2008).”
- Antiretroviral Therapy Optimisation without Genotype Resistance Testing: A Perspective on Treatment History Based Models (2010, background): “Accordingly, there is a plethora of GRT-based decision systems either for single drug susceptibility scoring or combined ART (cART) optimisation, from rule-based [23,24,25] to machine learning approaches [26,27,28,29,30,31,32].”
- History-alignment models for bias-aware prediction of virological response to HIV combination therapy (2012, background): “Some of these approaches (Bickel et al., 2008; Rosen-Zvi et al., 2008) incorporate information on the previous therapies administered in a patient’s history and thereby demonstrate the value of such knowledge.”
- Prediction of response to antiretroviral therapy by human experts and by the EuResist data‐driven expert system (the EVE study) (2010, unknown): “The prediction system combines three independent models into a classification of the treatment as a success or failure at 8 weeks [14].”