Personalizing medicine, by choosing therapies that maximize effectiveness and minimize side effects for individual patients, is one of the prime challenges in cancer treatment. At the core of personalized medicine is a machine learning problem: Given a set of patients whose response to some drugs has been observed, predict the response of a new patient or to a new drug. Computationally predicted responses can then be used to generate hypotheses for selecting therapies tailored to individual patients. However, the prediction task is exceedingly challenging, raising the need for the development of new machine learning methods. This thesis undertakes a unique multi-disciplinary approach to predict drug responses by utilizing multiple data sources in cancer, while simultaneously advancing the computational methods to improve accuracy. Specifically, the thesis presents a new Bayesian multi-view multi-task method that outperformed existing computational models in an international crowdsourcing challenge to predict drug responses. The method is further extended to solve the more challenging task of predicting drug responses in multiple cancer types. Notably, the thesis extends the kernelized Bayesian matrix factorization method with component-wise multiple kernel learning for effectively inferring associations between a large number of biologically motivated data sources and the latent factors. The results demonstrate that the new formulation of the method, supplemented with prior biological knowledge, is helpful for discovering interpretable associations as well as for predicting the drug responses of new cancer cells. The original contribution of this thesis is two-fold: First, the thesis proposes novel multi-view and multi-task methods to predict drug responses in cancer cells with increased accuracy. Second, new ways of incorporating prior biological knowledge are explored to further improve drug response predictions. Open source implementations of the new methods have been released to facilitate further research.
|Translated title of the contribution||Machine learning methods for improving drug response prediction in cancer|
|Publication status||Published - 2017|
|MoE publication type||G5 Doctoral dissertation (article)|
- data integration
- multi-view multi-task machine learning
- personalized medicine