In the machine learning world, the “Netflix problem” is a well-known computational challenge: Knowing how a viewer has rated movies they’ve watched, machine learning algorithms predict what other films they might like.
To make these kinds of predictions, computational experts have used tools called artificial neural networks, which are modeled on the organization of cells in the brain. The neural network learns patterns in existing data (for example, a viewer’s preference for comedy or action films) and estimates missing values such as the ratings a viewer might give for unseen movies, so that it can recommend new films the viewer might enjoy. The approach, however, falls short in applications with more complex datasets, such as the effects of untested drug candidates on gene activity in a variety of cell types.
