Predicting drug activity by image encoded gene expression profiles

Developing personalized cancer treatment procedures requires a prior knowledge on the effects of different drugs on cancer cell lines. While obtaining this information in vitro is a tedious task, the emergence of numerous large-scale datasets facilitates the usage of machine learning algorithms for this purpose. Conventional methods make an effort to reveal the mapping function between a cell line's identifying features called gene expressions and a certain drug's effect on it. In this work, we move away from this philosophy and represent cell lines as images in which inter-feature relations are preserved. Once these images are obtained, the regression problem is solved with the help of a convolutional neural network, a neural network architecture proven to work well with image inputs. A benchmarking with the other models in the literature exhibits the fruitfulness of our novel strategy.